Jie Xu


2024

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Contrastive Preference Learning for Neural Machine Translation
Jianfei He | Shichao Sun | Sen Peng | Jie Xu | Xiaohua Jia | Wenjie Li
Findings of the Association for Computational Linguistics: NAACL 2024

There exists a discrepancy between the token-level objective during training and the overall sequence-level quality that is expected from the model. This discrepancy leads to issues like exposure bias.To align the model with human expectations, sequence-level objectives are often used to fine-tune pre-trained models.In this paper, we introduce a contrastive preference model that enhances the traditional Plackett-Luce model by incorporating an indicator function. Building upon this novel preference model, we propose Contrastive Preference Learning (CPL), which uses offline samples with list-wise preferences to fine-tune a pre-trained model in Neural Machine Translation. Our experiments, conducted on three language pairs, demonstrate that CPL outperforms not only the vanilla Transformer model but also other token-level and sequence-level baselines. Furthermore, the ablation study highlights the essential role of the proposed indicator function in achieving this improvement.

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Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges
Xiaoming Shi | Zeming Liu | Li Du | Yuxuan Wang | Hongru Wang | Yuhang Guo | Tong Ruan | Jie Xu | Xiaofan Zhang | Shaoting Zhang
Findings of the Association for Computational Linguistics ACL 2024

This paper surveys and organizes research works of medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshape medical dialog systems’ foundation.Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists grand challenges of medical dialog systems, especially of large language models.

2020

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Neural Dialogue State Tracking with Temporally Expressive Networks
Junfan Chen | Richong Zhang | Yongyi Mao | Jie Xu
Findings of the Association for Computational Linguistics: EMNLP 2020

Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to improve the accuracy of turn-level-state prediction and the state aggregation.

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Parallel Interactive Networks for Multi-Domain Dialogue State Generation
Junfan Chen | Richong Zhang | Yongyi Mao | Jie Xu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) models. In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies. Specifically, we integrate an interactive encoder to jointly model the in-turn dependencies and cross-turn dependencies. The slot-level context is introduced to extract more expressive features for different slots. And a distributed copy mechanism is utilized to selectively copy words from historical system utterances or historical user utterances. Empirical studies demonstrated the superiority of the proposed PIN model.

2019

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Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework
Junfan Chen | Richong Zhang | Yongyi Mao | Hongyu Guo | Jie Xu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts. Nevertheless, most of the prior-art models for such tasks assume that the given text can be noisy, but their corresponding labels are clean. Such unrealistic assumption is contradictory with the fact that the given labels are often noisy as well, thus leading to significant performance degradation of those models on real-world data. To cope with this challenge, we propose a novel label-denoising framework that combines neural network with probabilistic modelling, which naturally takes into account the noisy labels during learning. We empirically demonstrate that our approach significantly improves the current art in uncovering the ground-truth relation labels.

2015

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Chinese CogBank: Where to See the Cognitive Features of Chinese Words
Bin Li | Xiaopeng Bai | Siqi Yin | Jie Xu
Proceedings of the Third Workshop on Metaphor in NLP

2003

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Focus-Marking in Chinese and Malay : A Comparative Perspective
Jie Xu
Proceedings of the 17th Pacific Asia Conference on Language, Information and Computation

1998

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Proceedings of the 12th Pacific Asia Conference on Language, Information and Computation
Jin Guo | Kim Teng Lua | Jie Xu
Proceedings of the 12th Pacific Asia Conference on Language, Information and Computation

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Grammatical Devices in the Processing of [+Wh] and [+Focus]
Jie Xu
Proceedings of the 12th Pacific Asia Conference on Language, Information and Computation