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The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions.
The measurement of observer agreement for categorical data
Landis, J. R.; and Koch, G. G. 1977 · 1977
Earlier work this paper cites.
A logic for default reasoning
Reiter, R. 1980 · 1980
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BLEU: a method for automatic evaluation of machine translation
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002 · 2002
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Lin, C.-Y. 2004 · 2004
Earlier work this paper cites.
Defending Against Neural Fake News
Zellers, R.; Holtzman, A.; Rashkin, H.; Bisk, Y.; Farhadi, A.; Roesner, F.; and Choi, Y. 2019 · 2012
Earlier work this paper cites.
Recognizing Textual Entailment: Models and Applications
Dagan, I.; Roth, D.; Sammons, M.; and Zanzotto, F. M. 2013 · 2013
Earlier work this paper cites.
The integrated mind
Gazzaniga, M. S.; and LeDoux, J. E. 2013 · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013 · 2013
Earlier work this paper cites.
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D.; Cho, K.; and Bengio, Y. 2015 · 2015
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Bowman, S.; Angeli, G.; Potts, C.; and Manning, C. D. 2015 · 2015
Earlier work this paper cites.
Rationalizing Neural Predictions
Lei, T.; Barzilay, R.; and Jaakkola, T. 2016 · 2016
Earlier work this paper cites.
” Why should I trust you?” Explaining the predictions of any classifier
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016 · 2016
Earlier work this paper cites.
spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing
Honnibal, M.; and Montani, I. 2017 · 2017
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Why We Need New Evaluation Metrics for NLG
Novikova, J.; Dušek, O.; Curry, A. C.; and Rieser, V. 2017 · 2017
Earlier work this paper cites.
Online and Linear-Time Attention by Enforcing Monotonic Alignments
Raffel, C.; Luong, M.; Liu, P. J.; Weiss, R. J.; and Eck, D. 2017 · 2017
Earlier work this paper cites.
ConceptNet 5.5: an open multilingual graph of general knowledge
Speer, R.; Chin, J.; and Havasi, C. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
e-SNLI: Natural Language Inference with Natural Language Explanations
Camburu, O.-M.; Rocktäschel, T.; Lukasiewicz, T.; and Blunsom, P. 2018 · 2018
Earlier work this paper cites.
Annotation Artifacts in Natural Language Inference Data
Gururangan, S.; Swayamdipta, S.; Levy, O.; Schwartz, R.; Bowman, S.; and Smith, N. A. 2018 · 2018
Earlier work this paper cites.
Hypothesis Only Baselines in Natural Language Inference
Poliak, A.; Naradowsky, J.; Haldar, A.; Rudinger, R.; and Van Durme, B. 2018 · 2018
Cited alongside, same era.
Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations
Shwartz, V.; and Dagan, I. 2018 · 2018
Cited alongside, same era.
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Williams, A.; Nangia, N.; and Bowman, S. 2018 · 2018
Cited alongside, same era.
Interpretable Neural Predictions with Differentiable Binary Variables
Bastings, J.; Aziz, W.; and Titov, I. 2019 · 2019
Cited alongside, same era.
Abductive Commonsense Reasoning
Bhagavatula, C.; Le Bras, R.; Malaviya, C.; Sakaguchi, K.; Holtzman, A.; Rashkin, H.; Downey, D.; Yih, W.-t.; and Choi, Y. 2019 · 2019
Cited alongside, same era.
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
Bosselut, A.; Rashkin, H.; Sap, M.; Malaviya, C.; Celikyilmaz, A.; and Choi, Y. 2019 · 2019
Attention is not not Explanation
Wiegreffe, S.; and Pinter, Y. 2019 · 2019
Later among the works it cites.
HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; and Brew, J. 2019 · 2019
Later among the works it cites.
A knowledge-enhanced pretraining model for commonsense story generation
Guan, J.; Huang, F.; Zhao, Z.; Zhu, X.; and Huang, M. 2020 · 2020
Closest in time.
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
Hase, P.; and Bansal, M. 2020 · 2020
Closest in time.
The Curious Case of Neural Text Degeneration
Holtzman, A.; Buys, J.; Du, L.; Forbes, M.; and Choi, Y. 2020 · 2020
Closest in time.
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?
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Cited alongside, same era.
Commonsense Knowledge Mining from Pretrained Models
Davison, J.; Feldman, J.; and Rush, A. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Cited alongside, same era.
Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
Dodge, J.; Liao, Q. V.; Zhang, Y.; Bellamy, R. K. E.; and Dugan, C. 2019 · 2019
Cited alongside, same era.
Attention is not Explanation
Jain, S.; and Wallace, B. C. 2019 · 2019
Cited alongside, same era.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 2019
Cited alongside, same era.
Language Models as Knowledge Bases?
Petroni, F.; Rocktäschel, T.; Riedel, S.; Lewis, P.; Bakhtin, A.; Wu, Y.; and Miller, A. 2019 · 2019
Cited alongside, same era.
Jacovi, A.; and Goldberg, Y. 2020 · 2020
Closest in time.
Learning to Faithfully Rationalize by Construction
Jain, S.; Wiegreffe, S.; Pinter, Y.; and Wallace, B. C. 2020 · 2020
Closest in time.
NILE : Natural Language Inference with Faithful Natural Language Explanations
Kumar, S.; and Talukdar, P. P. 2020 · 2020
Closest in time.
Explaining question answering models through text generation
Latcinnik, V.; and Berant, J. 2020 · 2020
Closest in time.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2020 · 2020
Closest in time.
WT5?! Training Text-to-Text Models to Explain their Predictions
Narang, S.; Raffel, C.; Lee, K.; Roberts, A.; Fiedel, N.; and Malkan, K. 2020 · 2020
Closest in time.
Adversarial NLI: A New Benchmark for Natural Language Understanding
Nie, Y.; Williams, A.; Dinan, E.; Bansal, M.; Weston, J.; and Kiela, D. 2020 · 2020
Closest in time.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
Closest in time.
Thinking Like a Skeptic: Defeasible Inference in Natural Language
Rudinger, R.; Shwartz, V.; Hwang, J. D.; Bhagavatula, C.; Forbes, M.; Le Bras, R.; Smith, N. A.; and Choi, Y. 2020 · 2020
Closest in time.
Unsupervised Commonsense Question Answering with Self-Talk
Shwartz, V.; West, P.; Le Bras, R.; Bhagavatula, C.; and Choi, Y. 2020 · 2020
Closest in time.
Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning
Tamborrino, A.; Pellicanò, N.; Pannier, B.; Voitot, P.; and Naudin, L. 2020 · 2020
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The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
Tenney, I.; Wexler, J.; Bastings, J.; Bolukbasi, T.; Coenen, A.; Gehrmann, S.; Jiang, E.; Pushkarna, M.; Radebaugh, C.; Reif, E.; and Yuan, A. 2020 · 2020
Closest in time.
Measuring Association Between Labels and Free-Text Rationales
Wiegreffe, Sarah and Marasović, Ana, and Smith, Noah A. 2020 · 2020
Closest in time.