Baobao Chang

Also published as: Bao-Bao Chang


2024

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Mitigating Language-Level Performance Disparity in mPLMs via Teacher Language Selection and Cross-lingual Self-Distillation
Haozhe Zhao | Zefan Cai | Shuzheng Si | Liang Chen | Yufeng He | Kaikai An | Baobao Chang
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Large-scale multilingual Pretrained Language Models (mPLMs) yield impressive performance on cross-language tasks, yet significant performance disparities exist across different languages within the same mPLM. Previous studies endeavored to narrow these disparities by supervise fine-tuning the mPLMs with multilingual data.However, obtaining labeled multilingual data is time-consuming, and fine-tuning mPLM with limited labeled multilingual data merely encapsulates the knowledge specific to the labeled data.Therefore, we introduce **ALSACE** to leverage the learned knowledge from the well-performing languages to guide under-performing ones within the same mPLM, eliminating the need for additional labeled multilingual data. Experiments show that ALSACE effectively mitigates language-level performance disparity across various mPLMs while showing the competitive performance on different multilingual NLU tasks, ranging from full resource to limited resource settings. The code for our approach is available at https://github.com/pkunlp-icler/ALSACE.

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DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade
Zefan Cai | Xin Zheng | Tianyu Liu | Haoran Meng | Jiaqi Han | Gang Yuan | Binghuai Lin | Baobao Chang | Yunbo Cao
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the existing data accumulated in the last updates. Within the newly added data, new intents would emerge and might have semantic entanglement with the existing intents, e.g. new intents that are semantically too specific or generic are actually a subset or superset of some existing intents in the semantic space, thus impairing the robustness of the NLU model.As the first attempt to solve this problem, we setup a new benchmark consisting of 4 Dialogue Version Control dataSets (DialogVCS). We formulate the intent detection with imperfect data in the system update as a multi-label classification task with positive but unlabeled intents, which asks the models to recognize all the proper intents, including the ones with semantic entanglement, in the inference.We also propose comprehensive baseline models and conduct in-depth analyses for the benchmark, showing that the semantically entangled intents can be effectively recognized with an automatic workflow. Our code and dataset are available at https://github.com/Zefan-Cai/DialogVCS.

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PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain
Liang Chen | Yichi Zhang | Shuhuai Ren | Haozhe Zhao | Zefan Cai | Yuchi Wang | Peiyi Wang | Xiangdi Meng | Tianyu Liu | Baobao Chang
Findings of the Association for Computational Linguistics ACL 2024

We present PCA-Bench, a multimodal decision-making benchmark for evaluating the integrated capabilities of Multimodal Large Language Models (MLLMs). Departing from previous benchmarks focusing on simplistic tasks and individual model capability, PCA-Bench introduces three complex scenarios: autonomous driving, domestic robotics, and open-world games. Given task instructions and diverse contexts, the model is required to seamlessly integrate multiple capabilities of Perception, Cognition, and Action in a reasoning chain to make accurate decisions. Moreover, PCA-Bench features error localization capabilities, scrutinizing model inaccuracies in areas such as perception, knowledge, or reasoning. This enhances the reliability of deploying MLLMs. To balance accuracy and efficiency in evaluation, we propose PCA-Eval, an automatic evaluation protocol, and assess 10 prevalent MLLMs. The results reveal significant performance disparities between open-source models and powerful proprietary models like GPT-4 Vision. To address this, we introduce Embodied-Instruction-Evolution (EIE), an automatic framework for synthesizing instruction tuning examples in multimodal embodied environments. EIE generates 7,510 training examples in PCA-Bench and enhances the performance of open-source MLLMs, occasionally surpassing GPT-4 Vision (+3% in decision accuracy), thereby validating the effectiveness of EIE. Our findings suggest that robust MLLMs like GPT4-Vision show promise for decision-making in embodied agents, opening new avenues for MLLM research. All benchmark data and evaluation code are made public.

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Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning
Shuzheng Si | Helan Hu | Haozhe Zhao | Shuang Zeng | Kaikai An | Zefan Cai | Baobao Chang
Findings of the Association for Computational Linguistics ACL 2024

Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the burden of annotation, but meanwhile suffers from the label noise. Recent works attempt to adopt the teacher-student framework to gradually refine the training labels and improve the overall robustness. However, we argue that these teacher-student methods achieve limited performance because the poor calibration of the teacher network produces incorrectly pseudo-labeled samples, leading to error propagation. Therefore, we attempt to mitigate this issue by proposing: (1) Uncertainty-Aware Teacher Learning that leverages the prediction uncertainty to reduce the number of incorrect pseudo labels in the self-training stage; (2) Student-Student Collaborative Learning that allows the transfer of reliable labels between two student networks instead of indiscriminately relying on all pseudo labels from its teacher. This approach further enables a full exploration of mislabeled samples rather than simply filtering unreliable pseudo-labeled samples. We evaluate our proposed method on five DS-NER datasets, demonstrating that our method is superior to the state-of-the-art DS-NER denoising methods.

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Improving Event Definition Following For Zero-Shot Event Detection
Zefan Cai | Po-Nien Kung | Ashima Suvarna | Mingyu Ma | Hritik Bansal | Baobao Chang | P. Jeffrey Brantingham | Wei Wang | Nanyun Peng
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches yield sporadic successes, yet generally fall short of expectations.In this work, we aim to improve zero-shot event detection by training models to better follow event definitions. We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types. To verify our hypothesis, we construct an automatically generated Diverse Event Definition (DivED) dataset and conduct comparative studies. Our experiments reveal that a large number of event types (200) and diverse event definitions can significantly boost event extraction performance; on the other hand, the performance does not scale with over ten examples per event type.Beyond scaling, we incorporate event ontology information and hard-negative samples during training, further boosting the performance. Based on these findings, we fine-tuned a LLaMA-2-7B model on our DivED dataset, yielding performance that surpasses SOTA large language models like GPT-3.5 across three open benchmarks on zero-shot event detection.

2023

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SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised Named Entity Recognition
Shuzheng Si | Zefan Cai | Shuang Zeng | Guoqiang Feng | Jiaxing Lin | Baobao Chang
Findings of the Association for Computational Linguistics: ACL 2023

Distantly-Supervised Named Entity Recognition effectively alleviates the burden of time-consuming and expensive annotation in the supervised setting. But the context-free matching process and the limited coverage of knowledge bases introduce inaccurate and incomplete annotation noise respectively. Previous studies either considered only incomplete one or indiscriminately handle two types of noise with the same strategy. In this paper, we argue that the different causes of two types of noise bring up the requirement of different strategies in model architecture. Therefore, we propose the SANTA to handle these two types of noise separately with (1) Memory-smoothed Focal Loss and Entity-aware KNN to relieve the entity ambiguity problem caused by inaccurate annotation, and (2) Boundary Mixup to alleviate decision boundary shifting problem caused by incomplete annotation and a noise-tolerant loss to improve the model’s robustness. Benefiting from our separate tailored strategies, we confirm in the experiment that the two types of noise are well mitigated.SANTA also achieves a new state-of-the-art on five public datasets.

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On the Off-Target Problem of Zero-Shot Multilingual Neural Machine Translation
Liang Chen | Shuming Ma | Dongdong Zhang | Furu Wei | Baobao Chang
Findings of the Association for Computational Linguistics: ACL 2023

While multilingual neural machine translation has achieved great success, it suffers from the off-target issue, where the translation is in the wrong language. This problem is more pronounced on zero-shot translation tasks. In this work, we find that failing in encoding discriminative target language signal will lead to off-target and a closer lexical distance (i.e., KL-divergence) between two languages’ vocabularies is related with a higher off-target rate. We also find that solely isolating the vocab of different languages in the decoder can alleviate the problem. Motivated by the findings, we propose Language Aware Vocabulary Sharing (LAVS), a simple and effective algorithm to construct the multilingual vocabulary, that greatly alleviates the off-target problem of the translation model by increasing the KL-divergence between languages. We conduct experiments on a multilingual machine translation benchmark in 11 languages. Experiments show that the off-target rate for 90 translation tasks is reduced from 29% to 8%, while the overall BLEU score is improved by an average of 1.9 points without extra training cost or sacrificing the supervised directions’ performance. We release the code at https://github.com/PKUnlp-icler/Off-Target-MNMT for reproduction.

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Guiding AMR Parsing with Reverse Graph Linearization
Bofei Gao | Liang Chen | Peiyi Wang | Zhifang Sui | Baobao Chang
Findings of the Association for Computational Linguistics: EMNLP 2023

Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence. The sequence-to-sequence approaches, which linearize the semantic graph into a sequence of nodes and edges and generate the linearized graph directly, have achieved good performance. However, we observed that these approaches suffer from structure loss accumulation during the decoding process, leading to a much lower F1-score for nodes and edges decoded later compared to those decoded earlier. To address this issue, we propose a novel Reverse Graph Linearization (RGL) enhanced framework. RGL defines both default and reverse linearization orders of an AMR graph, where most structures at the back part of the default order appear at the front part of the reversed order and vice versa. RGL incorporates the reversed linearization to the original AMR parser through a two-pass self-distillation mechanism, which guides the model when generating the default linearizations. Our analysis shows that our proposed method significantly mitigates the problem of structure loss accumulation, outperforming the previously best AMR parsing model by 0.8 and 0.5 Smatch scores on the AMR 2.0 and AMR 3.0 dataset, respectively. The code are available at https://github.com/pkunlp-icler/AMR_reverse_graph_linearization.

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Coarse-to-Fine Dual Encoders are Better Frame Identification Learners
Kaikai An | Ce Zheng | Bofei Gao | Haozhe Zhao | Baobao Chang
Findings of the Association for Computational Linguistics: EMNLP 2023

Frame identification aims to find semantic frames associated with target words in a sentence. Recent researches measure the similarity or matching score between targets and candidate frames by modeling frame definitions. However, they either lack sufficient representation learning of the definitions or face challenges in efficiently selecting the most suitable frame from over 1000 candidate frames. Moreover, commonly used lexicon filtering (lf) to obtain candidate frames for the target may ignore out-of-vocabulary targets and cause inadequate frame modeling. In this paper, we propose CoFFTEA, a  ̲Coarse-to- ̲Fine  ̲Frame and  ̲Target  ̲Encoders  ̲Architecture. With contrastive learning and dual encoders, CoFFTEA efficiently and effectively models the alignment between frames and targets. By employing a coarse-to-fine curriculum learning procedure, CoFFTEA gradually learns to differentiate frames with varying degrees of similarity. Experimental results demonstrate that CoFFTEA outperforms previous models by 0.93 overall scores and 1.53 R@1 without lf. Further analysis suggests that CoFFTEA can better model the relationships between frame and frame, as well as target and target. The code for our approach is available at https://github.com/pkunlp-icler/COFFTEA.

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Can We Edit Factual Knowledge by In-Context Learning?
Ce Zheng | Lei Li | Qingxiu Dong | Yuxuan Fan | Zhiyong Wu | Jingjing Xu | Baobao Chang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Previous studies have shown that large language models (LLMs) like GPTs store massive factual knowledge in their parameters. However, the stored knowledge could be false or outdated. Traditional knowledge editing methods refine LLMs via fine-tuning on texts containing specific knowledge. However, with the increasing scales of LLMs, these gradient-based approaches bring large computation costs. The trend of model-as-a-service also makes it impossible to modify knowledge in black-box LMs. Inspired by in-context learning (ICL), a new paradigm based on demonstration contexts without parameter updating, we explore whether ICL can edit factual knowledge. To answer this question, we give a comprehensive empirical study of ICL strategies. Experiments show that in-context knowledge editing (IKE), without any gradient and parameter updating, achieves a competitive success rate compared to gradient-based methods on GPT-J (6B) but with much fewer side effects, including less over-editing on similar but unrelated facts and less knowledge forgetting on previously stored knowledge. We also apply the method to larger LMs with tens or hundreds of parameters like OPT-175B, which shows the scalability of our method. The code is available at https://github.com/pkunlp-icler/IKE.

2022

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融合知识的多目标词联合框架语义分析模型(Knowledge-integrated Joint Model For Multi-target Frame Semantic Parsing)
Xudong Chen (陈旭东) | Ce Zheng (郑策) | Baobao Chang (常宝宝)
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“框架语义分析任务是自然语言处理领域的一项基础性任务。先前的研究工作大多针对单目标词进行模型设计,无法一次性完成多个目标词的框架语义结构提取。本文提出了一个面向多目标的框架语义分析模型,实现对多目标词的联合预测。该模型对框架语义分析的各项子任务进行交互性建模,实现子任务间的双向交互。此外,本文利用关系图网络对框架关系信息进行编码,将其作为框架语义学知识融入模型中。实验表明,本文模型在不借助额外语料的情况下相比之前模型都有不同程度的提高。消融实验证明了本文模型设计的有效性。此外我们分析了模型目前存在的局限性以及未来的改进方向。”

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生成,推理与排序:基于多任务架构的数学文字题生成(Generating, Reasoning & Ranking: Multitask Learning Framework for Math Word Problem Generation)
Tianyang Cao (曹天旸) | Xiaodan Xu (许晓丹) | Baobao Chang (常宝宝)
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“数学文字题是一段能反映数学等式潜在逻辑的叙述性文本。成功的数学问题生成在语言生成和教育领域都具有广阔的应用前景。前人的工作大多需要人工标注的模板或关键词作为输入,且未考虑数学表达式本身的特点。本文提出了一种多任务联合训练的问题文本生成模型。我们设计了三个辅助任务,包括数字间关系抽取、数值排序和片段替换预测。他们与生成目标联合训练,用以监督解码器的学习,增强模型对运算逻辑和问题条件的感知能力。实验证明所提方法能有效提升生成的数学文字题的质量。”

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ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
Liang Chen | Peiyi Wang | Runxin Xu | Tianyu Liu | Zhifang Sui | Baobao Chang
Findings of the Association for Computational Linguistics: NAACL 2022

As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing. We find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring more performance gain than other tasks e.g. MT and summarization in the text-to-AMR transition even with much less data. 2) To make a better fit for AMR, data from auxiliary tasks should be properly “AMRized” to PseudoAMR before training. Knowledge from shallow level parsing tasks can be better transferred to AMR Parsing with structure transform. 3) Intermediate-task learning is a better paradigm to introduce auxiliary tasks to AMR parsing, compared to multitask learning. From an empirical perspective, we propose a principled method to involve auxiliary tasks to boost AMR parsing. Extensive experiments show that our method achieves new state-of-the-art performance on different benchmarks especially in topology-related scores. Code and models are released at https://github.com/PKUnlp-icler/ATP.

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Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues
Qingxiu Dong | Ziwei Qin | Heming Xia | Tian Feng | Shoujie Tong | Haoran Meng | Lin Xu | Zhongyu Wei | Weidong Zhan | Baobao Chang | Sujian Li | Tianyu Liu | Zhifang Sui
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

It is a common practice for recent works in vision language cross-modal reasoning to adopt a binary or multi-choice classification formulation taking as input a set of source image(s) and textual query. In this work, we take a sober look at such an “unconditional” formulation in the sense that no prior knowledge is specified with respect to the source image(s). Inspired by the designs of both visual commonsense reasoning and natural language inference tasks, we propose a new task termed “Premise-based Multi-modal Reasoning” (PMR) where a textual premise is the background presumption on each source image. The PMR dataset contains 15,360 manually annotated samples which are created by a multi-phase crowd-sourcing process. With selected high-quality movie screenshots and human-curated premise templates from 6 pre-defined categories, we ask crowd-source workers to write one true hypothesis and three distractors (4 choices) given the premise and image through a cross-check procedure.

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StableMoE: Stable Routing Strategy for Mixture of Experts
Damai Dai | Li Dong | Shuming Ma | Bo Zheng | Zhifang Sui | Baobao Chang | Furu Wei
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The Mixture-of-Experts (MoE) technique can scale up the model size of Transformers with an affordable computational overhead. We point out that existing learning-to-route MoE methods suffer from the routing fluctuation issue, i.e., the target expert of the same input may change along with training, but only one expert will be activated for the input during inference. The routing fluctuation tends to harm sample efficiency because the same input updates different experts but only one is finally used. In this paper, we propose StableMoE with two training stages to address the routing fluctuation problem. In the first training stage, we learn a balanced and cohesive routing strategy and distill it into a lightweight router decoupled from the backbone model. In the second training stage, we utilize the distilled router to determine the token-to-expert assignment and freeze it for a stable routing strategy. We validate our method on language modeling and multilingual machine translation. The results show that StableMoE outperforms existing MoE methods in terms of both convergence speed and performance.

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CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Ningyu Zhang | Mosha Chen | Zhen Bi | Xiaozhuan Liang | Lei Li | Xin Shang | Kangping Yin | Chuanqi Tan | Jian Xu | Fei Huang | Luo Si | Yuan Ni | Guotong Xie | Zhifang Sui | Baobao Chang | Hui Zong | Zheng Yuan | Linfeng Li | Jun Yan | Hongying Zan | Kunli Zhang | Buzhou Tang | Qingcai Chen
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually offering great promise for medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.

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Knowledge Neurons in Pretrained Transformers
Damai Dai | Li Dong | Yaru Hao | Zhifang Sui | Baobao Chang | Furu Wei
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Large-scale pretrained language models are surprisingly good at recalling factual knowledge presented in the training corpus. In this paper, we present preliminary studies on how factual knowledge is stored in pretrained Transformers by introducing the concept of knowledge neurons. Specifically, we examine the fill-in-the-blank cloze task for BERT. Given a relational fact, we propose a knowledge attribution method to identify the neurons that express the fact. We find that the activation of such knowledge neurons is positively correlated to the expression of their corresponding facts. In our case studies, we attempt to leverage knowledge neurons to edit (such as update, and erase) specific factual knowledge without fine-tuning. Our results shed light on understanding the storage of knowledge within pretrained Transformers.

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Hierarchical Curriculum Learning for AMR Parsing
Peiyi Wang | Liang Chen | Tianyu Liu | Damai Dai | Yunbo Cao | Baobao Chang | Zhifang Sui
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, and is recently empowered by pretrained sequence-to-sequence models. However, there exists a gap between their flat training objective (i.e., equally treats all output tokens) and the hierarchical AMR structure, which limits the model generalization. To bridge this gap, we propose a Hierarchical Curriculum Learning (HCL) framework with Structure-level (SC) and Instance-level Curricula (IC). SC switches progressively from core to detail AMR semantic elements while IC transits from structure-simple to -complex AMR instances during training. Through these two warming-up processes, HCL reduces the difficulty of learning complex structures, thus the flat model can better adapt to the AMR hierarchy. Extensive experiments on AMR2.0, AMR3.0, structure-complex and out-of-distribution situations verify the effectiveness of HCL.

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S4-Tuning: A Simple Cross-lingual Sub-network Tuning Method
Runxin Xu | Fuli Luo | Baobao Chang | Songfang Huang | Fei Huang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

The emergence of multilingual pre-trained language models makes it possible to adapt to target languages with only few labeled examples. However, vanilla fine-tuning tends to achieve degenerated and unstable results, owing to the Language Interference among different languages, and Parameter Overload under the few-sample transfer learning scenarios. To address two problems elegantly, we propose S4-Tuning, a Simple Cross-lingual Sub-network Tuning method. S4-Tuning first detects the most essential sub-network for each target language, and only updates it during fine-tuning.In this way, the language sub-networks lower the scale of trainable parameters, and hence better suit the low-resource scenarios.Meanwhile, the commonality and characteristics across languages are modeled by the overlapping and non-overlapping parts to ease the interference among languages.Simple but effective, S4-Tuning gains consistent improvements over vanilla fine-tuning on three multi-lingual tasks involving 37 different languages in total (XNLI, PAWS-X, and Tatoeba).

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Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation
Liang Chen | Runxin Xu | Baobao Chang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Label smoothing and vocabulary sharing are two widely used techniques in neural machine translation models. However, we argue that simply applying both techniques can be conflicting and even leads to sub-optimal performance. When allocating smoothed probability, original label smoothing treats the source-side words that would never appear in the target language equally to the real target-side words, which could bias the translation model. To address this issue, we propose Masked Label Smoothing (MLS), a new mechanism that masks the soft label probability of source-side words to zero. Simple yet effective, MLS manages to better integrate label smoothing with vocabulary sharing. Our extensive experiments show that MLS consistently yields improvement over original label smoothing on different datasets, including bilingual and multilingual translation from both translation quality and model’s calibration. Our code is released at https://github.com/PKUnlp-icler/MLS

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SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER
Shuzheng Si | Shuang Zeng | Jiaxing Lin | Baobao Chang
Proceedings of the 29th International Conference on Computational Linguistics

Unlabeled Entity Problem (UEP) in Named Entity Recognition (NER) datasets seriously hinders the improvement of NER performance. This paper proposes SCL-RAI to cope with this problem. Firstly, we decrease the distance of span representations with the same label while increasing it for different ones via span-based contrastive learning, which relieves the ambiguity among entities and improves the robustness of the model over unlabeled entities. Then we propose retrieval augmented inference to mitigate the decision boundary shifting problem. Our method significantly outperforms the previous SOTA method by 4.21% and 8.64% F1-score on two real-world datasets.

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DISK: Domain-constrained Instance Sketch for Math Word Problem Generation
Tianyang Cao | Shuang Zeng | Xiaodan Xu | Mairgup Mansur | Baobao Chang
Proceedings of the 29th International Conference on Computational Linguistics

A math word problem (MWP) is a coherent narrative which reflects the underlying logic of math equations. Successful MWP generation can automate the writing of mathematics questions. Previous methods mainly generate MWP text based on inflexible pre-defined templates. In this paper, we propose a neural model for generating MWP text from math equations. Firstly, we incorporate a matching model conditioned on the domain knowledge to retrieve a MWP instance which is most consistent with the ground-truth, where the domain is a latent variable extracted with a domain summarizer. Secondly, by constructing a Quantity Cell Graph (QCG) from the retrieved MWP instance and reasoning over it, we improve the model’s comprehension of real-world scenarios and derive a domain-constrained instance sketch to guide the generation. Besides, the QCG also interacts with the equation encoder to enhance the alignment between math tokens (e.g., quantities and variables) and MWP text. Experiments and empirical analysis on educational MWP set show that our model achieves impressive performance in both automatic evaluation metrics and human evaluation metrics.

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Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting
Shuzheng Si | Shuang Zeng | Baobao Chang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Incomplete utterance rewriting has recently raised wide attention. However, previous works do not consider the semantic structural information between incomplete utterance and rewritten utterance or model the semantic structure implicitly and insufficiently. To address this problem, we propose a QUEry-Enhanced Network(QUEEN) to solve this problem. Firstly, our proposed query template explicitly brings guided semantic structural knowledge between the incomplete utterance and the rewritten utterance making model perceive where to refer back to or recover omitted tokens. Then, we adopt a fast and effective edit operation scoring network to model the relation between two tokens. Benefiting from extra information and the well-designed network, QUEEN achieves state-of-the-art performance on several public datasets.

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A Double-Graph Based Framework for Frame Semantic Parsing
Ce Zheng | Xudong Chen | Runxin Xu | Baobao Chang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Frame semantic parsing is a fundamental NLP task, which consists of three subtasks: frame identification, argument identification and role classification. Most previous studies tend to neglect relations between different subtasks and arguments and pay little attention to ontological frame knowledge defined in FrameNet. In this paper, we propose a Knowledge-guided Incremental semantic parser with Double-graph (KID). We first introduce Frame Knowledge Graph (FKG), a heterogeneous graph containing both frames and FEs (Frame Elements) built on the frame knowledge so that we can derive knowledge-enhanced representations for frames and FEs. Besides, we propose Frame Semantic Graph (FSG) to represent frame semantic structures extracted from the text with graph structures. In this way, we can transform frame semantic parsing into an incremental graph construction problem to strengthen interactions between subtasks and relations between arguments. Our experiments show that KID outperforms the previous state-of-the-art method by up to 1.7 F1-score on two FrameNet datasets. Our code is availavle at https://github.com/PKUnlp-icler/KID.

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An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling
Peiyi Wang | Runxin Xu | Tianyu Liu | Qingyu Zhou | Yunbo Cao | Baobao Chang | Zhifang Sui
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain. Recently, the metric-based meta-learning framework has been recognized as a promising approach for FSSL. However, most prior works assign a label to each token based on the token-level similarities, which ignores the integrality of named entities or slots. To this end, in this paper, we propose ESD, an Enhanced Span-based Decomposition method for FSSL. ESD formulates FSSL as a span-level matching problem between test query and supporting instances. Specifically, ESD decomposes the span matching problem into a series of span-level procedures, mainly including enhanced span representation, class prototype aggregation and span conflicts resolution. Extensive experiments show that ESD achieves the new state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is proven to be more robust in the noisy and nested tagging scenarios.

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A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
Runxin Xu | Peiyi Wang | Tianyu Liu | Shuang Zeng | Baobao Chang | Zhifang Sui
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the long-distance dependency between trigger and arguments over sentences; b) the distracting context towards an event in the document. To address these issues, we propose a Two-Stream Abstract meaning Representation enhanced extraction model (TSAR). TSAR encodes the document from different perspectives by a two-stream encoding module, to utilize local and global information and lower the impact of distracting context. Besides, TSAR introduces an AMR-guided interaction module to capture both intra-sentential and inter-sentential features, based on the locally and globally constructed AMR semantic graphs. An auxiliary boundary loss is introduced to enhance the boundary information for text spans explicitly. Extensive experiments illustrate that TSAR outperforms previous state-of-the-art by a large margin, with 2.54 F1 and 5.13 F1 performance gain on the public RAMS and WikiEvents datasets respectively, showing the superiority in the cross-sentence arguments extraction. We release our code in https://github.com/PKUnlp-icler/TSAR.

2021

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Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions
Damai Dai | Hua Zheng | Fuli Luo | Pengcheng Yang | Tianyu Liu | Zhifang Sui | Baobao Chang
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and we need to efficiently represent these entities. Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG. To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Moreover, given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method even needs no additional learning. Experimental results on two KGC tasks with OOKG entities show that our method outperforms the previous methods by a large margin with higher efficiency.

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Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation
Hua Zheng | Damai Dai | Lei Li | Tianyu Liu | Zhifang Sui | Baobao Chang | Yang Liu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word. Most existing methods take the source word as an indecomposable semantic unit. However, in parataxis languages like Chinese, word meanings can be composed using the word formation process, where a word (“桃花”, peach-blossom) is formed by formation components (“桃”, peach; “花”, flower) using a formation rule (Modifier-Head). Inspired by this process, we propose to enhance DG with word formation features. We build a formation-informed dataset, and propose a model DeFT, which Decomposes words into formation features, dynamically Fuses different features through a gating mechanism, and generaTes word definitions. Experimental results show that our method is both effective and robust.

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Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning
Runxin Xu | Fuli Luo | Zhiyuan Zhang | Chuanqi Tan | Baobao Chang | Songfang Huang | Fei Huang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent pretrained language models extend from millions to billions of parameters. Thus the need to fine-tune an extremely large pretrained model with a limited training corpus arises in various downstream tasks. In this paper, we propose a straightforward yet effective fine-tuning technique, Child-Tuning, which updates a subset of parameters (called child network) of large pretrained models via strategically masking out the gradients of the non-child network during the backward process. Experiments on various downstream tasks in GLUE benchmark show that Child-Tuning consistently outperforms the vanilla fine-tuning by 1.5 8.6 average score among four different pretrained models, and surpasses the prior fine-tuning techniques by 0.6 1.3 points. Furthermore, empirical results on domain transfer and task transfer show that Child-Tuning can obtain better generalization performance by large margins.

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SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction
Shuang Zeng | Yuting Wu | Baobao Chang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Joint Multi-Decoder Framework with Hierarchical Pointer Network for Frame Semantic Parsing
Xudong Chen | Ce Zheng | Baobao Chang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker
Runxin Xu | Tianyu Liu | Lei Li | Baobao Chang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences; b) the correlation among events in a document is non-trivial to model. In this paper, we propose Heterogeneous Graph-based Interaction Model with a Tracker (GIT) to solve the aforementioned two challenges. For the first challenge, GIT constructs a heterogeneous graph interaction network to capture global interactions among different sentences and entity mentions. For the second, GIT introduces a Tracker module to track the extracted events and hence capture the interdependency among the events. Experiments on a large-scale dataset (Zheng et al, 2019) show GIT outperforms the previous methods by 2.8 F1. Further analysis reveals is effective in extracting multiple correlated events and event arguments that scatter across the document.

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基于双编码器的医学文本中文分词(Chinese word segmentation of medical text based on dual-encoder)
Yuan Zong (宗源) | Baobao Chang (常宝宝)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

中文分词是自然语言处理领域的基础工作,然而前人的医学文本分词工作都只是直接套用通用分词的方法,而医学文本多专用术语的特点让分词系统需要对医学专用术语和医学文本中的非医学术语文本提供不同的分词粒度。本文提出了双编码器医学文本中文分词模型,利用辅助编码器为医学专有术语提供粗粒度表示。模型将需要粗粒度分词的医学专用术语和需要通用分词粒度的文本分开,在提升医学专用术语的分词能力的同时最大限度地避免了其粗粒度对于医学文本中通用文本分词的干扰。

2020

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面向医学文本处理的医学实体标注规范(Medical Entity Annotation Standard for Medical Text Processing)
Huan Zhang (张欢) | Yuan Zong (宗源) | Baobao Chang (常宝宝) | Zhifang Sui (穗志方) | Hongying Zan (昝红英) | Kunli Zhang (张坤丽)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

随着智慧医疗的普及,利用自然语言处理技术识别医学信息的需求日益增长。目前,针对医学实体而言,医学共享语料库仍处于空白状态,这对医学文本信息处理各项任务的进展造成了巨大阻力。如何判断不同的医学实体类别?如何界定不同实体间的涵盖范围?这些问题导致缺乏类似通用场景的大规模规范标注的医学文本数据。针对上述问题,该文参考了UMLS中定义的语义类型,提出面向医学文本信息处理的医学实体标注规范,涵盖了疾病、临床表现、医疗程序、医疗设备等9种医学实体,以及基于规范构建医学实体标注语料库。该文综述了标注规范的描述体系、分类原则、混淆处理、语料标注过程以及医学实体自动标注基线实验等相关问题,希望能为医学实体语料库的构建提供可参考的标注规范,以及为医学实体识别提供语料支持。

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An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference
Tianyu Liu | Zheng Xin | Xiaoan Ding | Baobao Chang | Zhifang Sui
Proceedings of the 24th Conference on Computational Natural Language Learning

The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust. In this paper, we focus on the model-agnostic debiasing strategies and explore how to (or is it possible to) make the NLI models robust to multiple distinct adversarial attacks while keeping or even strengthening the models’ generalization power. We firstly benchmark prevailing neural NLI models including pretrained ones on various adversarial datasets. We then try to combat distinct known biases by modifying a mixture of experts (MoE) ensemble method and show that it’s nontrivial to mitigate multiple NLI biases at the same time, and that model-level ensemble method outperforms MoE ensemble method. We also perform data augmentation including text swap, word substitution and paraphrase and prove its efficiency in combating various (though not all) adversarial attacks at the same time. Finally, we investigate several methods to merge heterogeneous training data (1.35M) and perform model ensembling, which are straightforward but effective to strengthen NLI models.

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HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference
Tianyu Liu | Zheng Xin | Baobao Chang | Zhifang Sui
Proceedings of the Twelfth Language Resources and Evaluation Conference

Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise. In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. Specifically, we extract various phrases from the hypotheses (artificial patterns) in the training sets, and show that they have been strong indicators to the specific labels. We then figure out ‘hard’ and ‘easy’ instances from the original test sets whose labels are opposite to or consistent with those indications. We also set up baselines including both pretrained models (BERT, RoBerta, XLNet) and competitive non-pretrained models (InferSent, DAM, ESIM). Apart from the benchmark and baselines, we also investigate two debiasing approaches which exploit the artificial pattern modeling to mitigate such hypothesis-only bias: down-sampling and adversarial training. We believe those methods can be treated as competitive baselines in NLI debiasing tasks.

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An Anchor-Based Automatic Evaluation Metric for Document Summarization
Kexiang Wang | Tianyu Liu | Baobao Chang | Zhifang Sui
Proceedings of the 28th International Conference on Computational Linguistics

The widespread adoption of reference-based automatic evaluation metrics such as ROUGE has promoted the development of document summarization. In this paper, we consider a new protocol for designing reference-based metrics that require the endorsement of source document(s). Following protocol, we propose an anchored ROUGE metric fixing each summary particle on source document, which bases the computation on more solid ground. Empirical results on benchmark datasets validate that source document helps to induce a higher correlation with human judgments for ROUGE metric. Being self-explanatory and easy-to-implement, the protocol can naturally foster various effective designs of reference-based metrics besides the anchored ROUGE introduced here.

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A Spectral Method for Unsupervised Multi-Document Summarization
Kexiang Wang | Baobao Chang | Zhifang Sui
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Multi-document summarization (MDS) aims at producing a good-quality summary for several related documents. In this paper, we propose a spectral-based hypothesis, which states that the goodness of summary candidate is closely linked to its so-called spectral impact. Here spectral impact considers the perturbation to the dominant eigenvalue of affinity matrix when dropping the summary candidate from the document cluster. The hypothesis is validated by three theoretical perspectives: semantic scaling, propagation dynamics and matrix perturbation. According to the hypothesis, we formulate the MDS task as the combinatorial optimization of spectral impact and propose an accelerated greedy solution based on a surrogate of spectral impact. The evaluation results on various datasets demonstrate: (1) The performance of the summary candidate is positively correlated with its spectral impact, which accords with our hypothesis; (2) Our spectral-based method has a competitive result as compared to state-of-the-art MDS systems.

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Double Graph Based Reasoning for Document-level Relation Extraction
Shuang Zeng | Runxin Xu | Baobao Chang | Lei Li
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Document-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multiple sentences across paragraphs. In this paper, we propose Graph Aggregation-and-Inference Network (GAIN), a method to recognize such relations for long paragraphs. GAIN constructs two graphs, a heterogeneous mention-level graph (MG) and an entity-level graph (EG). The former captures complex interaction among different mentions and the latter aggregates mentions underlying for the same entities. Based on the graphs we propose a novel path reasoning mechanism to infer relations between entities. Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. Our code is available at https://github.com/PKUnlp-icler/GAIN.

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Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference
Xiaoan Ding | Tianyu Liu | Baobao Chang | Zhifang Sui | Kevin Gimpel
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

While discriminative neural network classifiers are generally preferred, recent work has shown advantages of generative classifiers in term of data efficiency and robustness. In this paper, we focus on natural language inference (NLI). We propose GenNLI, a generative classifier for NLI tasks, and empirically characterize its performance by comparing it to five baselines, including discriminative models and large-scale pretrained language representation models like BERT. We explore training objectives for discriminative fine-tuning of our generative classifiers, showing improvements over log loss fine-tuning from prior work (Lewis and Fan, 2019). In particular, we find strong results with a simple unbounded modification to log loss, which we call the “infinilog loss”. Our experiments show that GenNLI outperforms both discriminative and pretrained baselines across several challenging NLI experimental settings, including small training sets, imbalanced label distributions, and label noise.

2019

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Towards Fine-grained Text Sentiment Transfer
Fuli Luo | Peng Li | Pengcheng Yang | Jie Zhou | Yutong Tan | Baobao Chang | Zhifang Sui | Xu Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we focus on the task of fine-grained text sentiment transfer (FGST). This task aims to revise an input sequence to satisfy a given sentiment intensity, while preserving the original semantic content. Different from the conventional sentiment transfer task that only reverses the sentiment polarity (positive/negative) of text, the FTST task requires more nuanced and fine-grained control of sentiment. To remedy this, we propose a novel Seq2SentiSeq model. Specifically, the numeric sentiment intensity value is incorporated into the decoder via a Gaussian kernel layer to finely control the sentiment intensity of the output. Moreover, to tackle the problem of lacking parallel data, we propose a cycle reinforcement learning algorithm to guide the model training. In this framework, the elaborately designed rewards can balance both sentiment transformation and content preservation, while not requiring any ground truth output. Experimental results show that our approach can outperform existing methods by a large margin in both automatic evaluation and human evaluation.

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Towards Comprehensive Description Generation from Factual Attribute-value Tables
Tianyu Liu | Fuli Luo | Pengcheng Yang | Wei Wu | Baobao Chang | Zhifang Sui
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The comprehensive descriptions for factual attribute-value tables, which should be accurate, informative and loyal, can be very helpful for end users to understand the structured data in this form. However previous neural generators might suffer from key attributes missing, less informative and groundless information problems, which impede the generation of high-quality comprehensive descriptions for tables. To relieve these problems, we first propose force attention (FA) method to encourage the generator to pay more attention to the uncovered attributes to avoid potential key attributes missing. Furthermore, we propose reinforcement learning for information richness to generate more informative as well as more loyal descriptions for tables. In our experiments, we utilize the widely used WIKIBIO dataset as a benchmark. Besides, we create WB-filter based on WIKIBIO to test our model in the simulated user-oriented scenarios, in which the generated descriptions should accord with particular user interests. Experimental results show that our model outperforms the state-of-the-art baselines on both automatic and human evaluation.

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Learning to Control the Fine-grained Sentiment for Story Ending Generation
Fuli Luo | Damai Dai | Pengcheng Yang | Tianyu Liu | Baobao Chang | Zhifang Sui | Xu Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Automatic story ending generation is an interesting and challenging task in natural language generation. Previous studies are mainly limited to generate coherent, reasonable and diversified story endings, and few works focus on controlling the sentiment of story endings. This paper focuses on generating a story ending which meets the given fine-grained sentiment intensity. There are two major challenges to this task. First is the lack of story corpus which has fine-grained sentiment labels. Second is the difficulty of explicitly controlling sentiment intensity when generating endings. Therefore, we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges. The sentiment analyzer adopts a series of methods to acquire sentiment intensities of the story dataset. The sentimental generator introduces the sentiment intensity into decoder via a Gaussian Kernel Layer to control the sentiment of the output. To the best of our knowledge, this is the first endeavor to control the fine-grained sentiment for story ending generation without manually annotating sentiment labels. Experiments show that our proposed framework can generate story endings which are not only more coherent and fluent but also able to meet the given sentiment intensity better.

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Pun-GAN: Generative Adversarial Network for Pun Generation
Fuli Luo | Shunyao Li | Pengcheng Yang | Lei Li | Baobao Chang | Zhifang Sui | Xu Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In this paper, we focus on the task of generating a pun sentence given a pair of word senses. A major challenge for pun generation is the lack of large-scale pun corpus to guide supervised learning. To remedy this, we propose an adversarial generative network for pun generation (Pun-GAN). It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses. The output of the discriminator is then used as a reward to train the generator via reinforcement learning, encouraging it to produce pun sentences which can support two word senses simultaneously. Experiments show that the proposed Pun-GAN can generate sentences that are more ambiguous and diverse in both automatic and human evaluation.

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A Soft Label Strategy for Target-Level Sentiment Classification
Da Yin | Xiao Liu | Xiuyu Wu | Baobao Chang
Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

In this paper, we propose a soft label approach to target-level sentiment classification task, in which a history-based soft labeling model is proposed to measure the possibility of a context word as an opinion word. We also apply a convolution layer to extract local active features, and introduce positional weights to take relative distance information into consideration. In addition, we obtain more informative target representation by training with context tokens together to make deeper interaction between target and context tokens. We conduct experiments on SemEval 2014 datasets and the experimental results show that our approach significantly outperforms previous models and gives state-of-the-art results on these datasets.

2018

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Incorporating Glosses into Neural Word Sense Disambiguation
Fuli Luo | Tianyu Liu | Qiaolin Xia | Baobao Chang | Zhifang Sui
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Word Sense Disambiguation (WSD) aims to identify the correct meaning of polysemous words in the particular context. Lexical resources like WordNet which are proved to be of great help for WSD in the knowledge-based methods. However, previous neural networks for WSD always rely on massive labeled data (context), ignoring lexical resources like glosses (sense definitions). In this paper, we integrate the context and glosses of the target word into a unified framework in order to make full use of both labeled data and lexical knowledge. Therefore, we propose GAS: a gloss-augmented WSD neural network which jointly encodes the context and glosses of the target word. GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods. We further extend the original gloss of word sense via its semantic relations in WordNet to enrich the gloss information. The experimental results show that our model outperforms the state-of-the-art systems on several English all-words WSD datasets.

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EventWiki: A Knowledge Base of Major Events
Tao Ge | Lei Cui | Baobao Chang | Zhifang Sui | Furu Wei | Ming Zhou
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention
Fuli Luo | Tianyu Liu | Zexue He | Qiaolin Xia | Zhifang Sui | Baobao Chang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The goal of Word Sense Disambiguation (WSD) is to identify the correct meaning of a word in the particular context. Traditional supervised methods only use labeled data (context), while missing rich lexical knowledge such as the gloss which defines the meaning of a word sense. Recent studies have shown that incorporating glosses into neural networks for WSD has made significant improvement. However, the previous models usually build the context representation and gloss representation separately. In this paper, we find that the learning for the context and gloss representation can benefit from each other. Gloss can help to highlight the important words in the context, thus building a better context representation. Context can also help to locate the key words in the gloss of the correct word sense. Therefore, we introduce a co-attention mechanism to generate co-dependent representations for the context and gloss. Furthermore, in order to capture both word-level and sentence-level information, we extend the attention mechanism in a hierarchical fashion. Experimental results show that our model achieves the state-of-the-art results on several standard English all-words WSD test datasets.

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Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition
Tao Ge | Qing Dou | Heng Ji | Lei Cui | Baobao Chang | Zhifang Sui | Furu Wei | Ming Zhou
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the networks for accurate text stream alignment. Experiments on Chinese-English news streams show our approach not only outperforms previous approaches on bilingual lexicon extraction from coordinated text streams but also can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining, which makes it promising to be a new paradigm for automatic language knowledge acquisition.

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Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data
Wenhui Wang | Baobao Chang | Mairgup Mansur
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Pre-trained word embeddings and language model have been shown useful in a lot of tasks. However, both of them cannot directly capture word connections in a sentence, which is important for dependency parsing given its goal is to establish dependency relations between words. In this paper, we propose to implicitly capture word connections from unlabeled data by a word ordering model with self-attention mechanism. Experiments show that these implicit word connections do improve our parsing model. Furthermore, by combining with a pre-trained language model, our model gets state-of-the-art performance on the English PTB dataset, achieving 96.35% UAS and 95.25% LAS.

2017

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Syntax Aware LSTM model for Semantic Role Labeling
Feng Qian | Lei Sha | Baobao Chang | Lu-chen Liu | Ming Zhang
Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing

In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models. In this paper, we propose Syntax Aware Long Short Time Memory (SA-LSTM). The structure of SA-LSTM changes according to dependency structure of each sentence, so that SA-LSTM can model the whole tree structure of dependency relation in an architecture engineering way. Experiments demonstrate that on Chinese Proposition Bank (CPB) 1.0, SA-LSTM improves F1 by 2.06% than ordinary bi-LSTM with feature engineered dependency relation information, and gives state-of-the-art F1 of 79.92%. On English CoNLL 2005 dataset, SA-LSTM brings improvement (2.1%) to bi-LSTM model and also brings slight improvement (0.3%) when added to the state-of-the-art model.

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Affinity-Preserving Random Walk for Multi-Document Summarization
Kexiang Wang | Tianyu Liu | Zhifang Sui | Baobao Chang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint of summarization in the random walk process. The ROUGE evaluations on DUC 2003 topic-focused summarization task and DUC 2004 generic summarization task show the good performance of our method, which has the best ROUGE-2 recall among the graph-based ranking methods.

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A Soft-label Method for Noise-tolerant Distantly Supervised Relation Extraction
Tianyu Liu | Kexiang Wang | Baobao Chang | Zhifang Sui
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Distant-supervised relation extraction inevitably suffers from wrong labeling problems because it heuristically labels relational facts with knowledge bases. Previous sentence level denoise models don’t achieve satisfying performances because they use hard labels which are determined by distant supervision and immutable during training. To this end, we introduce an entity-pair level denoise method which exploits semantic information from correctly labeled entity pairs to correct wrong labels dynamically during training. We propose a joint score function which combines the relational scores based on the entity-pair representation and the confidence of the hard label to obtain a new label, namely a soft label, for certain entity pair. During training, soft labels instead of hard labels serve as gold labels. Experiments on the benchmark dataset show that our method dramatically reduces noisy instances and outperforms other state-of-the-art systems.

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Gated Self-Matching Networks for Reading Comprehension and Question Answering
Wenhui Wang | Nan Yang | Furu Wei | Baobao Chang | Ming Zhou
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we present the gated self-matching networks for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation. Then we propose a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage. We finally employ the pointer networks to locate the positions of answers from the passages. We conduct extensive experiments on the SQuAD dataset. The single model achieves 71.3% on the evaluation metrics of exact match on the hidden test set, while the ensemble model further boosts the results to 75.9%. At the time of submission of the paper, our model holds the first place on the SQuAD leaderboard for both single and ensemble model.

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A Progressive Learning Approach to Chinese SRL Using Heterogeneous Data
Qiaolin Xia | Lei Sha | Baobao Chang | Zhifang Sui
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Previous studies on Chinese semantic role labeling (SRL) have concentrated on a single semantically annotated corpus. But the training data of single corpus is often limited. Whereas the other existing semantically annotated corpora for Chinese SRL are scattered across different annotation frameworks. But still, Data sparsity remains a bottleneck. This situation calls for larger training datasets, or effective approaches which can take advantage of highly heterogeneous data. In this paper, we focus mainly on the latter, that is, to improve Chinese SRL by using heterogeneous corpora together. We propose a novel progressive learning model which augments the Progressive Neural Network with Gated Recurrent Adapters. The model can accommodate heterogeneous inputs and effectively transfer knowledge between them. We also release a new corpus, Chinese SemBank, for Chinese SRL. Experiments on CPB 1.0 show that our model outperforms state-of-the-art methods.

2016

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RBPB: Regularization-Based Pattern Balancing Method for Event Extraction
Lei Sha | Jing Liu | Chin-Yew Lin | Sujian Li | Baobao Chang | Zhifang Sui
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Graph-based Dependency Parsing with Bidirectional LSTM
Wenhui Wang | Baobao Chang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Joint Learning Templates and Slots for Event Schema Induction
Lei Sha | Sujian Li | Baobao Chang | Zhifang Sui
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Discourse Parsing with Attention-based Hierarchical Neural Networks
Qi Li | Tianshi Li | Baobao Chang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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News Stream Summarization using Burst Information Networks
Tao Ge | Lei Cui | Baobao Chang | Sujian Li | Ming Zhou | Zhifang Sui
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Capturing Argument Relationship for Chinese Semantic Role Labeling
Lei Sha | Sujian Li | Baobao Chang | Zhifang Sui | Tingsong Jiang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Encoding Temporal Information for Time-Aware Link Prediction
Tingsong Jiang | Tianyu Liu | Tao Ge | Lei Sha | Sujian Li | Baobao Chang | Zhifang Sui
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Towards Time-Aware Knowledge Graph Completion
Tingsong Jiang | Tianyu Liu | Tao Ge | Lei Sha | Baobao Chang | Sujian Li | Zhifang Sui
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Knowledge graph (KG) completion adds new facts to a KG by making inferences from existing facts. Most existing methods ignore the time information and only learn from time-unknown fact triples. In dynamic environments that evolve over time, it is important and challenging for knowledge graph completion models to take into account the temporal aspects of facts. In this paper, we present a novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts. To incorporate the happening time of facts, we propose a time-aware KG embedding model using temporal order information among facts. To incorporate the valid time of facts, we propose a joint time-aware inference model based on Integer Linear Programming (ILP) using temporal consistencyinformationasconstraints. Wefurtherintegratetwomodelstomakefulluseofglobal temporal information. We empirically evaluate our models on time-aware KG completion task. Experimental results show that our time-aware models achieve the state-of-the-art on temporal facts consistently.

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Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition
Lei Sha | Baobao Chang | Zhifang Sui | Sujian Li
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Recognizing Textual Entailment (RTE) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate deep neural network methods for the RTE task. Previous neural network based methods usually try to encode the two sentences (premise and hypothesis) and send them together into a multi-layer perceptron to get their entailment type, or use LSTM-RNN to link two sentences together while using attention mechanic to enhance the model’s ability. In this paper, we propose to use the re-read mechanic, which means to read the premise again and again while reading the hypothesis. After read the premise again, the model can get a better understanding of the premise, which can also affect the understanding of the hypothesis. On the contrary, a better understanding of the hypothesis can also affect the understanding of the premise. With the alternative re-read process, the model can “think” of a better decision of entailment type. We designed a new LSTM unit called re-read LSTM (rLSTM) to implement this “thinking” process. Experiments show that we achieve results better than current state-of-the-art equivalents.

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Event Detection with Burst Information Networks
Tao Ge | Lei Cui | Baobao Chang | Zhifang Sui | Ming Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Retrospective event detection is an important task for discovering previously unidentified events in a text stream. In this paper, we propose two fast centroid-aware event detection models based on a novel text stream representation – Burst Information Networks (BINets) for addressing the challenge. The BINets are time-aware, efficient and can be easily analyzed for identifying key information (centroids). These advantages allow the BINet-based approaches to achieve the state-of-the-art performance on multiple datasets, demonstrating the efficacy of BINets for the task of event detection.

2015

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An Effective Neural Network Model for Graph-based Dependency Parsing
Wenzhe Pei | Tao Ge | Baobao Chang
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Bring you to the past: Automatic Generation of Topically Relevant Event Chronicles
Tao Ge | Wenzhe Pei | Heng Ji | Sujian Li | Baobao Chang | Zhifang Sui
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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One Tense per Scene: Predicting Tense in Chinese Conversations
Tao Ge | Heng Ji | Baobao Chang | Zhifang Sui
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Multi-label Text Categorization with Joint Learning Predictions-as-Features Method
Li Li | Houfeng Wang | Xu Sun | Baobao Chang | Shi Zhao | Lei Sha
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Recognizing Textual Entailment Using Probabilistic Inference
Lei Sha | Sujian Li | Baobao Chang | Zhifang Sui | Tingsong Jiang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks
Zhen Wang | Tingsong Jiang | Baobao Chang | Zhifang Sui
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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ERSOM: A Structural Ontology Matching Approach Using Automatically Learned Entity Representation
Chuncheng Xiang | Tingsong Jiang | Baobao Chang | Zhifang Sui
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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A Joint Model for Unsupervised Chinese Word Segmentation
Miaohong Chen | Baobao Chang | Wenzhe Pei
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Max-Margin Tensor Neural Network for Chinese Word Segmentation
Wenzhe Pei | Tao Ge | Baobao Chang
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Inducing Word Sense with Automatically Learned Hidden Concepts
Baobao Chang | Wenzhe Pei | Miaohong Chen
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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SSMT:A Machine Translation Evaluation View To Paragraph-to-Sentence Semantic Similarity
Pingping Huang | Baobao Chang
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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Event-Based Time Label Propagation for Automatic Dating of News Articles
Tao Ge | Baobao Chang | Sujian Li | Zhifang Sui
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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A Maximum Entropy Approach to Chinese Spelling Check
Dongxu Han | Baobao Chang
Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing

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Feature-based Neural Language Model and Chinese Word Segmentation
Mairgup Mansur | Wenzhe Pei | Baobao Chang
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Update Summarization using a Multi-level Hierarchical Dirichlet Process Model
Jiwei Li | Sujian Li | Xun Wang | Ye Tian | Baobao Chang
Proceedings of COLING 2012

2010

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Enhancing Domain Portability of Chinese Segmentation Model Using Chi-Square Statistics and Bootstrapping
Baobao Chang | Dongxu Han
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Chinese word segmentation model using bootstrapping
Baobao Chang | Mansur Mairgup
CIPS-SIGHAN Joint Conference on Chinese Language Processing

2008

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Improving Chinese Semantic Role Classification with Hierarchical Feature Selection Strategy
Weiwei Ding | Baobao Chang
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2005

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Extracting Terminologically Relevant Collocations in the Translation of Chinese Monograph
Byeong-Kwu Kang | Bao-Bao Chang | Yi-Rong Chen | Shi-Wen Yu
Second International Joint Conference on Natural Language Processing: Full Papers

2004

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Chinese-English Parallel Corpus Construction and its Application
Baobao Chang
Proceedings of the 18th Pacific Asia Conference on Language, Information and Computation

2003

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Chinese Word Segmentation at Peking University
Huiming Duan | Xiaojing Bai | Baobao Chang | Shiwen Yu
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

2002

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Extraction of Translation Unit from Chinese-English Parallel Corpora
Baobao Chang | Pernilla Danielsson | Wolfgang Teubert
COLING-02: The First SIGHAN Workshop on Chinese Language Processing

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