Yanai Elazar


2024

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Proceedings of the 1st Workshop on Data Contamination (CONDA)
Oscar Sainz | Iker García Ferrero | Eneko Agirre | Jon Ander Campos | Alon Jacovi | Yanai Elazar | Yoav Goldberg
Proceedings of the 1st Workshop on Data Contamination (CONDA)

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Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz | Iker García-Ferrero | Alon Jacovi | Jon Ander Campos | Yanai Elazar | Eneko Agirre | Yoav Goldberg | Wei-Lin Chen | Jenny Chim | Leshem Choshen | Luca D’Amico-Wong | Melissa Dell | Run-Ze Fan | Shahriar Golchin | Yucheng Li | Pengfei Liu | Bhavish Pahwa | Ameya Prabhu | Suryansh Sharma | Emily Silcock | Kateryna Solonko | David Stap | Mihai Surdeanu | Yu-Min Tseng | Vishaal Udandarao | Zengzhi Wang | Ruijie Xu | Jinglin Yang
Proceedings of the 1st Workshop on Data Contamination (CONDA)

The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora used to train large scale models, compromising evaluation results. The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models. The goal of the shared task and associated database is to assist the community in understanding the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. The shared task provides a structured, centralized public database for the collection of contamination evidence, open to contributions from the community via GitHub pool requests. This first compilation paper is based on 566 reported entries over 91 contaminated sources from a total of 23 contributors. The details of the individual contamination events are available in the platform. The platform continues to be online, open to contributions from the community.

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The Bias Amplification Paradox in Text-to-Image Generation
Preethi Seshadri | Sameer Singh | Yanai Elazar
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Bias amplification is a phenomenon in which models exacerbate biases or stereotypes present in the training data. In this paper, we study bias amplification in the text-to-image domain using Stable Diffusion by comparing gender ratios in training vs. generated images. We find that the model appears to amplify gender-occupation biases found in the training data (LAION) considerably. However, we discover that amplification can be largely attributed to discrepancies between training captions and model prompts. For example, an inherent difference is that captions from the training data often contain explicit gender information while our prompts do not, which leads to a distribution shift and consequently inflates bias measures. Once we account for distributional differences between texts used for training and generation when evaluating amplification, we observe that amplification decreases drastically. Our findings illustrate the challenges of comparing biases in models and their training data, as well as evaluation more broadly, and highlight how confounding factors can impact analyses.

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Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Luca Soldaini | Rodney Kinney | Akshita Bhagia | Dustin Schwenk | David Atkinson | Russell Authur | Ben Bogin | Khyathi Chandu | Jennifer Dumas | Yanai Elazar | Valentin Hofmann | Ananya Jha | Sachin Kumar | Li Lucy | Xinxi Lyu | Nathan Lambert | Ian Magnusson | Jacob Morrison | Niklas Muennighoff | Aakanksha Naik | Crystal Nam | Matthew Peters | Abhilasha Ravichander | Kyle Richardson | Zejiang Shen | Emma Strubell | Nishant Subramani | Oyvind Tafjord | Evan Walsh | Luke Zettlemoyer | Noah Smith | Hannaneh Hajishirzi | Iz Beltagy | Dirk Groeneveld | Jesse Dodge | Kyle Lo
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to reproduce them. As a result, it is challenging to conduct and advance scientific research on language modeling, such as understanding how training data impacts model capabilities and limitations. To facilitate scientific research on language model pretraining, we curate and release Dolma, a three-trillion-token English corpus, built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopedic materials. We extensively document Dolma, including its design principles, details about its construction, and a summary of its contents. We present analyses and experimental results on intermediate states of Dolma to share what we have learned about important data curation practices. Finally, we open-source our data curation toolkit to enable reproduction of our work as well as support further research in large-scale data curation.

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OLMo: Accelerating the Science of Language Models
Dirk Groeneveld | Iz Beltagy | Evan Walsh | Akshita Bhagia | Rodney Kinney | Oyvind Tafjord | Ananya Jha | Hamish Ivison | Ian Magnusson | Yizhong Wang | Shane Arora | David Atkinson | Russell Authur | Khyathi Chandu | Arman Cohan | Jennifer Dumas | Yanai Elazar | Yuling Gu | Jack Hessel | Tushar Khot | William Merrill | Jacob Morrison | Niklas Muennighoff | Aakanksha Naik | Crystal Nam | Matthew Peters | Valentina Pyatkin | Abhilasha Ravichander | Dustin Schwenk | Saurabh Shah | William Smith | Emma Strubell | Nishant Subramani | Mitchell Wortsman | Pradeep Dasigi | Nathan Lambert | Kyle Richardson | Luke Zettlemoyer | Jesse Dodge | Kyle Lo | Luca Soldaini | Noah Smith | Hannaneh Hajishirzi
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, we have built OLMo, a competitive, truly Open Language Model, to enable the scientific study of language models. Unlike most prior efforts that have only released model weights and inference code, we release OLMo alongside open training data and training and evaluation code. We hope this release will empower the open research community and inspire a new wave of innovation.

2023

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CIKQA: Learning Commonsense Inference with a Unified Knowledge-in-the-loop QA Paradigm
Hongming Zhang | Yintong Huo | Yanai Elazar | Yangqiu Song | Yoav Goldberg | Dan Roth
Findings of the Association for Computational Linguistics: EACL 2023

We propose a new commonsense reasoning benchmark to motivate commonsense reasoning progress from two perspectives: (1) Evaluating whether models can distinguish knowledge quality by predicting if the knowledge is enough to answer the question; (2) Evaluating whether models can develop commonsense inference capabilities that generalize across tasks. We first extract supporting knowledge for each question and ask humans to annotate whether the auto-extracted knowledge is enough to answer the question or not. After that, we convert different tasks into a unified question-answering format to evaluate the models’ generalization capabilities. We name the benchmark Commonsense Inference with Knowledge-in-the-loop Question Answering (\name). Experiments show that with our learning paradigm, models demonstrate encouraging generalization capabilities. At the same time, we also notice that distinguishing knowledge quality remains challenging for current commonsense reasoning models.

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Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation
Marius Mosbach | Tiago Pimentel | Shauli Ravfogel | Dietrich Klakow | Yanai Elazar
Findings of the Association for Computational Linguistics: ACL 2023

Few-shot fine-tuning and in-context learning are two alternative strategies for task adaptation of pre-trained language models. Recently, in-context learning has gained popularity over fine-tuning due to its simplicity and improved out-of-domain generalization, and because extensive evidence shows that fine-tuned models pick up on spurious correlations. Unfortunately, previous comparisons of the two approaches were done using models of different sizes. This raises the question of whether the observed weaker out-of-domain generalization of fine-tuned models is an inherent property of fine-tuning or a limitation of the experimental setup. In this paper, we compare the generalization of few-shot fine-tuning and in-context learning to challenge datasets, while controlling for the models used, the number of examples, and the number of parameters, ranging from 125M to 30B. Our results show that fine-tuned language models can in fact generalize well out-of-domain. We find that both approaches generalize similarly; they exhibit large variation and depend on properties such as model size and the number of examples, highlighting that robust task adaptation remains a challenge.

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Proceedings of the Big Picture Workshop
Yanai Elazar | Allyson Ettinger | Nora Kassner | Sebastian Ruder | Noah A. Smith
Proceedings of the Big Picture Workshop

2022

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Lexical Generalization Improves with Larger Models and Longer Training
Elron Bandel | Yoav Goldberg | Yanai Elazar
Findings of the Association for Computational Linguistics: EMNLP 2022

While fine-tuned language models perform well on many language tasks, they were also shown to rely on superficial surface features such as lexical overlap. Excessive utilization of such heuristics can lead to failure on challenging inputs. We analyze the use of lexical overlap heuristics in natural language inference, paraphrase detection, and reading comprehension (using a novel contrastive dataset),and find that larger models are much less susceptible to adopting lexical overlap heuristics. We also find that longer training leads models to abandon lexical overlap heuristics. Finally, We provide evidence that the disparity between models size has its source in the pre-trained model.

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Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Jasmijn Bastings | Yonatan Belinkov | Yanai Elazar | Dieuwke Hupkes | Naomi Saphra | Sarah Wiegreffe
Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

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Text-based NP Enrichment
Yanai Elazar | Victoria Basmov | Yoav Goldberg | Reut Tsarfaty
Transactions of the Association for Computational Linguistics, Volume 10

Understanding the relations between entities denoted by NPs in a text is a critical part of human-like natural language understanding. However, only a fraction of such relations is covered by standard NLP tasks and benchmarks nowadays. In this work, we propose a novel task termed text-based NP enrichment (TNE), in which we aim to enrich each NP in a text with all the preposition-mediated relations—either explicit or implicit—that hold between it and other NPs in the text. The relations are represented as triplets, each denoted by two NPs related via a preposition. Humans recover such relations seamlessly, while current state-of-the-art models struggle with them due to the implicit nature of the problem. We build the first large-scale dataset for the problem, provide the formal framing and scope of annotation, analyze the data, and report the results of fine-tuned language models on the task, demonstrating the challenge it poses to current technology. A webpage with a data-exploration UI, a demo, and links to the code, models, and leaderboard, to foster further research into this challenging problem can be found at: yanaiela.github.io/TNE/.

2021

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First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
Benjamin Muller | Yanai Elazar | Benoît Sagot | Djamé Seddah
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Multilingual pretrained language models have demonstrated remarkable zero-shot cross-lingual transfer capabilities. Such transfer emerges by fine-tuning on a task of interest in one language and evaluating on a distinct language, not seen during the fine-tuning. Despite promising results, we still lack a proper understanding of the source of this transfer. Using a novel layer ablation technique and analyses of the model’s internal representations, we show that multilingual BERT, a popular multilingual language model, can be viewed as the stacking of two sub-networks: a multilingual encoder followed by a task-specific language-agnostic predictor. While the encoder is crucial for cross-lingual transfer and remains mostly unchanged during fine-tuning, the task predictor has little importance on the transfer and can be reinitialized during fine-tuning. We present extensive experiments with three distinct tasks, seventeen typologically diverse languages and multiple domains to support our hypothesis.

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Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals
Yanai Elazar | Shauli Ravfogel | Alon Jacovi | Yoav Goldberg
Transactions of the Association for Computational Linguistics, Volume 9

A growing body of work makes use of probing in order to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitations of the probing paradigm. In this work, we point out the inability to infer behavioral conclusions from probing results, and offer an alternative method that focuses on how the information is being used, rather than on what information is encoded. Our method, Amnesic Probing, follows the intuition that the utility of a property for a given task can be assessed by measuring the influence of a causal intervention that removes it from the representation. Equipped with this new analysis tool, we can ask questions that were not possible before, for example, is part-of-speech information important for word prediction? We perform a series of analyses on BERT to answer these types of questions. Our findings demonstrate that conventional probing performance is not correlated to task importance, and we call for increased scrutiny of claims that draw behavioral or causal conclusions from probing results.1

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Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes
Ofer Sabo | Yanai Elazar | Yoav Goldberg | Ido Dagan
Transactions of the Association for Computational Linguistics, Volume 9

We explore few-shot learning (FSL) for relation classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, [NOTA]), we first revisit the recent popular dataset structure for FSL, pointing out its unrealistic data distribution. To remedy this, we propose a novel methodology for deriving more realistic few-shot test data from available datasets for supervised RC, and apply it to the TACRED dataset. This yields a new challenging benchmark for FSL-RC, on which state of the art models show poor performance. Next, we analyze classification schemes within the popular embedding-based nearest-neighbor approach for FSL, with respect to constraints they impose on the embedding space. Triggered by this analysis, we propose a novel classification scheme in which the NOTA category is represented as learned vectors, shown empirically to be an appealing option for FSL.

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Measuring and Improving Consistency in Pretrained Language Models
Yanai Elazar | Nora Kassner | Shauli Ravfogel | Abhilasha Ravichander | Eduard Hovy | Hinrich Schütze | Yoav Goldberg
Transactions of the Association for Computational Linguistics, Volume 9

Consistency of a model—that is, the invariance of its behavior under meaning-preserving alternations in its input—is a highly desirable property in natural language processing. In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect to factual knowledge? To this end, we create ParaRel🤘, a high-quality resource of cloze-style query English paraphrases. It contains a total of 328 paraphrases for 38 relations. Using ParaRel🤘, we show that the consistency of all PLMs we experiment with is poor— though with high variance between relations. Our analysis of the representational spaces of PLMs suggests that they have a poor structure and are currently not suitable for representing knowledge robustly. Finally, we propose a method for improving model consistency and experimentally demonstrate its effectiveness.1

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Erratum: Measuring and Improving Consistency in Pretrained Language Models
Yanai Elazar | Nora Kassner | Shauli Ravfogel | Abhilasha Ravichander | Eduard Hovy | Hinrich Schütze | Yoav Goldberg
Transactions of the Association for Computational Linguistics, Volume 9

During production of this paper, an error was introduced to the formula on the bottom of the right column of page 1020. In the last two terms of the formula, the n and m subscripts were swapped. The correct formula is:Lc=∑n=1k∑m=n+1kDKL(Qnri∥Qmri)+DKL(Qmri∥Qnri)The paper has been updated.

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Contrastive Explanations for Model Interpretability
Alon Jacovi | Swabha Swayamdipta | Shauli Ravfogel | Yanai Elazar | Yejin Choi | Yoav Goldberg
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Contrastive explanations clarify why an event occurred in contrast to another. They are inherently intuitive to humans to both produce and comprehend. We propose a method to produce contrastive explanations in the latent space, via a projection of the input representation, such that only the features that differentiate two potential decisions are captured. Our modification allows model behavior to consider only contrastive reasoning, and uncover which aspects of the input are useful for and against particular decisions. Our contrastive explanations can additionally answer for which label, and against which alternative label, is a given input feature useful. We produce contrastive explanations via both high-level abstract concept attribution and low-level input token/span attribution for two NLP classification benchmarks. Our findings demonstrate the ability of label-contrastive explanations to provide fine-grained interpretability of model decisions.

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Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema
Yanai Elazar | Hongming Zhang | Yoav Goldberg | Dan Roth
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The Winograd Schema (WS) has been proposed as a test for measuring commonsense capabilities of models. Recently, pre-trained language model-based approaches have boosted performance on some WS benchmarks but the source of improvement is still not clear. This paper suggests that the apparent progress on WS may not necessarily reflect progress in commonsense reasoning. To support this claim, we first show that the current evaluation method of WS is sub-optimal and propose a modification that uses twin sentences for evaluation. We also propose two new baselines that indicate the existence of artifacts in WS benchmarks. We then develop a method for evaluating WS-like sentences in a zero-shot setting to account for the commonsense reasoning abilities acquired during the pretraining and observe that popular language models perform randomly in this setting when using our more strict evaluation. We conclude that the observed progress is mostly due to the use of supervision in training WS models, which is not likely to successfully support all the required commonsense reasoning skills and knowledge.

2020

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The Extraordinary Failure of Complement Coercion Crowdsourcing
Yanai Elazar | Victoria Basmov | Shauli Ravfogel | Yoav Goldberg | Reut Tsarfaty
Proceedings of the First Workshop on Insights from Negative Results in NLP

Crowdsourcing has eased and scaled up the collection of linguistic annotation in recent years. In this work, we follow known methodologies of collecting labeled data for the complement coercion phenomenon. These are constructions with an implied action — e.g., “I started a new book I bought last week”, where the implied action is reading. We aim to collect annotated data for this phenomenon by reducing it to either of two known tasks: Explicit Completion and Natural Language Inference. However, in both cases, crowdsourcing resulted in low agreement scores, even though we followed the same methodologies as in previous work. Why does the same process fail to yield high agreement scores? We specify our modeling schemes, highlight the differences with previous work and provide some insights about the task and possible explanations for the failure. We conclude that specific phenomena require tailored solutions, not only in specialized algorithms, but also in data collection methods.

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Evaluating Models’ Local Decision Boundaries via Contrast Sets
Matt Gardner | Yoav Artzi | Victoria Basmov | Jonathan Berant | Ben Bogin | Sihao Chen | Pradeep Dasigi | Dheeru Dua | Yanai Elazar | Ananth Gottumukkala | Nitish Gupta | Hannaneh Hajishirzi | Gabriel Ilharco | Daniel Khashabi | Kevin Lin | Jiangming Liu | Nelson F. Liu | Phoebe Mulcaire | Qiang Ning | Sameer Singh | Noah A. Smith | Sanjay Subramanian | Reut Tsarfaty | Eric Wallace | Ally Zhang | Ben Zhou
Findings of the Association for Computational Linguistics: EMNLP 2020

Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture the abilities a dataset is intended to test. We propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model’s decision boundary, which can be used to more accurately evaluate a model’s true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, and IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets—up to 25% in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes.

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Do Language Embeddings capture Scales?
Xikun Zhang | Deepak Ramachandran | Ian Tenney | Yanai Elazar | Dan Roth
Findings of the Association for Computational Linguistics: EMNLP 2020

Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.

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oLMpics-On What Language Model Pre-training Captures
Alon Talmor | Yanai Elazar | Yoav Goldberg | Jonathan Berant
Transactions of the Association for Computational Linguistics, Volume 8

Recent success of pre-trained language models (LMs) has spurred widespread interest in the language capabilities that they possess. However, efforts to understand whether LM representations are useful for symbolic reasoning tasks have been limited and scattered. In this work, we propose eight reasoning tasks, which conceptually require operations such as comparison, conjunction, and composition. A fundamental challenge is to understand whether the performance of a LM on a task should be attributed to the pre-trained representations or to the process of fine-tuning on the task data. To address this, we propose an evaluation protocol that includes both zero-shot evaluation (no fine-tuning), as well as comparing the learning curve of a fine-tuned LM to the learning curve of multiple controls, which paints a rich picture of the LM capabilities. Our main findings are that: (a) different LMs exhibit qualitatively different reasoning abilities, e.g., RoBERTa succeeds in reasoning tasks where BERT fails completely; (b) LMs do not reason in an abstract manner and are context-dependent, e.g., while RoBERTa can compare ages, it can do so only when the ages are in the typical range of human ages; (c) On half of our reasoning tasks all models fail completely. Our findings and infrastructure can help future work on designing new datasets, models, and objective functions for pre-training.

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It’s not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT
Hila Gonen | Shauli Ravfogel | Yanai Elazar | Yoav Goldberg
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages. We study the word-level translation information embedded in mBERT and present two simple methods that expose remarkable translation capabilities with no fine-tuning. The results suggest that most of this information is encoded in a non-linear way, while some of it can also be recovered with purely linear tools. As part of our analysis, we test the hypothesis that mBERT learns representations which contain both a language-encoding component and an abstract, cross-lingual component, and explicitly identify an empirical language-identity subspace within mBERT representations.

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Unsupervised Distillation of Syntactic Information from Contextualized Word Representations
Shauli Ravfogel | Yanai Elazar | Jacob Goldberger | Yoav Goldberg
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Contextualized word representations, such as ELMo and BERT, were shown to perform well on various semantic and syntactic task. In this work, we tackle the task of unsupervised disentanglement between semantics and structure in neural language representations: we aim to learn a transformation of the contextualized vectors, that discards the lexical semantics, but keeps the structural information. To this end, we automatically generate groups of sentences which are structurally similar but semantically different, and use metric-learning approach to learn a transformation that emphasizes the structural component that is encoded in the vectors. We demonstrate that our transformation clusters vectors in space by structural properties, rather than by lexical semantics. Finally, we demonstrate the utility of our distilled representations by showing that they outperform the original contextualized representations in a few-shot parsing setting.

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Do Language Embeddings capture Scales?
Xikun Zhang | Deepak Ramachandran | Ian Tenney | Yanai Elazar | Dan Roth
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.

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Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection
Shauli Ravfogel | Yanai Elazar | Hila Gonen | Michael Twiton | Yoav Goldberg
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models. We present Iterative Null-space Projection (INLP), a novel method for removing information from neural representations. Our method is based on repeated training of linear classifiers that predict a certain property we aim to remove, followed by projection of the representations on their null-space. By doing so, the classifiers become oblivious to that target property, making it hard to linearly separate the data according to it. While applicable for multiple uses, we evaluate our method on bias and fairness use-cases, and show that our method is able to mitigate bias in word embeddings, as well as to increase fairness in a setting of multi-class classification.

2019

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How Large Are Lions? Inducing Distributions over Quantitative Attributes
Yanai Elazar | Abhijit Mahabal | Deepak Ramachandran | Tania Bedrax-Weiss | Dan Roth
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as “Is a lion bigger than a wolf?”. Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.

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Adversarial Removal of Demographic Attributes Revisited
Maria Barrett | Yova Kementchedjhieva | Yanai Elazar | Desmond Elliott | Anders Søgaard
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Elazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier on a held-out subsample of the data it was trained on. We revisit their experiments and conduct a series of follow-up experiments showing that, in fact, the diagnostic classifier generalizes poorly to both new in-domain samples and new domains, indicating that it relies on correlations specific to their particular data sample. We further show that a diagnostic classifier trained on the biased baseline neural network also does not generalize to new samples. In other words, the biases detected in Elazar and Goldberg (2018) seem restricted to their particular data sample, and would therefore not bias the decisions of the model on new samples, whether in-domain or out-of-domain. In light of this, we discuss better methodologies for detecting bias in our models.

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Where’s My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution
Yanai Elazar | Yoav Goldberg
Transactions of the Association for Computational Linguistics, Volume 7

We provide the first computational treatment of fused-heads constructions (FHs), focusing on the numeric fused-heads (NFHs). FHs constructions are noun phrases in which the head noun is missing and is said to be “fused” with its dependent modifier. This missing information is implicit and is important for sentence understanding. The missing references are easily filled in by humans but pose a challenge for computational models. We formulate the handling of FHs as a two stages process: Identification of the FH construction and resolution of the missing head. We explore the NFH phenomena in large corpora of English text and create (1) a data set and a highly accurate method for NFH identification; (2) a 10k examples (1 M tokens) crowd-sourced data set of NFH resolution; and (3) a neural baseline for the NFH resolution task. We release our code and data set, to foster further research into this challenging problem.

2018

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Adversarial Removal of Demographic Attributes from Text Data
Yanai Elazar | Yoav Goldberg
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in—and can be recovered from—the intermediate representations learned by text-based neural classifiers. The implication is that decisions of classifiers trained on textual data are not agnostic to—and likely condition on—demographic attributes. When attempting to remove such demographic information using adversarial training, we find that while the adversarial component achieves chance-level development-set accuracy during training, a post-hoc classifier, trained on the encoded sentences from the first part, still manages to reach substantially higher classification accuracies on the same data. This behavior is consistent across several tasks, demographic properties and datasets. We explore several techniques to improve the effectiveness of the adversarial component. Our main conclusion is a cautionary one: do not rely on the adversarial training to achieve invariant representation to sensitive features.
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