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Pointer Sentinel Mixture ModelsPreprint

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Adversarial Examples for Evaluating Reading Comprehension Systems

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Deep Variational Information BottleneckPreprint

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HotFlip: White-Box Adversarial Examples for Text Classification

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Rationalizing Neural Predictions

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Is Attention Interpretable?

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Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

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Synthetic and Natural Noise Both Break Neural Machine TranslationPreprint

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Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?

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Attention is not ExplanationPreprint

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Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences

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Learning the Difference that Makes a Difference with Counterfactually-Augmented DataPreprint

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On the Robustness of Interpretability Methods

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A causal framework for explaining the predictions of black-box sequence-to-sequence models

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Learning to Faithfully Rationalize by Construction

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WT5?! Training Text-to-Text Models to Explain their PredictionsPreprint

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Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control

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Measuring Association Between Labels and Free-Text Rationales

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Do Feature Attribution Methods Correctly Attribute Features?

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An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction

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Invariant RationalizationPreprint

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Look at the First Sentence: Position Bias in Question Answering

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Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport

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SPECTRA: Sparse Structured Text Rationalization

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Rationales for Sequential Predictions

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LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured PredictionPreprint

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Learning from the Best: Rationalizing Prediction by Adversarial Information CalibrationPreprint

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Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations

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Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting

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Discovering Spatio-Temporal Rationales for Video Question Answering

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MCC-KD: Multi-CoT Consistent Knowledge Distillation

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Rethinking Document-Level Relation Extraction: A Reality Check

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MGR: Multi-generator Based Rationalization

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Human-annotated rationales and explainable text classification: a survey

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Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture

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Enhancing the Rationale-Input Alignment for Self-explaining Rationalization

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Explanation-based Finetuning Makes Models More Robust to Spurious Cues

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Rationale-Enhanced Language Models are Better Continual Relation Learners

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Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations

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Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)

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Prompting for explanations improves Adversarial NLI. Is this true? {Yes} it is {true} because {it weakens superficial cues}

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Complex Reasoning in Natural Languag

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The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models

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Learning to Ignore Adversarial Attacks

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Are Triggers Needed for Document-Level Event Extraction?

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Boosting Explainability through Selective Rationalization in Pre-trained Language Models

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Exploring Practical Gaps in Using Cross Entropy to Implement Maximum Mutual Information Criterion for Rationalization

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FedRNL: Federated Rationalization with Soft Parameter Sharing

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Explaining Clinical Outcome Predictions with Compositional Rationale Extraction Model

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Does Self-Rationalization Improve Robustness to Spurious Correlations?

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