Rationales for Sequential Predictions
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[1] Motoaki Kawanabe,et al. How to Explain Individual Classification Decisions , 2009, J. Mach. Learn. Res..
[2] Le Song,et al. Learning to Explain: An Information-Theoretic Perspective on Model Interpretation , 2018, ICML.
[3] B. Arnold,et al. Compatible Conditional Distributions , 1989 .
[4] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[5] Shi Feng,et al. Pathologies of Neural Models Make Interpretations Difficult , 2018, EMNLP.
[6] Byron C. Wallace,et al. Attention is not Explanation , 2019, NAACL.
[7] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[8] Yindalon Aphinyanagphongs,et al. Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations , 2021, AISTATS.
[9] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[10] Sandro Pezzelle,et al. The LAMBADA dataset: Word prediction requiring a broad discourse context , 2016, ACL.
[11] Robert L. Mercer,et al. The Mathematics of Statistical Machine Translation: Parameter Estimation , 1993, CL.
[12] Alexander Binder,et al. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.
[13] Ivan Titov,et al. Interpretable Neural Predictions with Differentiable Binary Variables , 2019, ACL.
[14] Kyunghyun Cho,et al. Rissanen Data Analysis: Examining Dataset Characteristics via Description Length , 2021, ICML.
[15] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[16] Regina Barzilay,et al. Rationalizing Neural Predictions , 2016, EMNLP.
[17] Manaal Faruqui,et al. Attention Interpretability Across NLP Tasks , 2019, ArXiv.
[18] Byron C. Wallace,et al. ERASER: A Benchmark to Evaluate Rationalized NLP Models , 2020, ACL.
[19] Grzegorz Chrupala,et al. Representation of Linguistic Form and Function in Recurrent Neural Networks , 2016, CL.
[20] Myle Ott,et al. fairseq: A Fast, Extensible Toolkit for Sequence Modeling , 2019, NAACL.
[21] Balaraman Ravindran,et al. Towards Transparent and Explainable Attention Models , 2020, ACL.
[22] Daniel Jurafsky,et al. Understanding Neural Networks through Representation Erasure , 2016, ArXiv.
[23] Willem Zuidema,et al. Quantifying Attention Flow in Transformers , 2020, ACL.
[24] Ivan Titov,et al. Interpretable Neural Predictions with Differentiable Binary Variables , 2019, ACL.
[25] Rico Sennrich,et al. Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation , 2020, ACL.
[26] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[27] Xinlei Chen,et al. Visualizing and Understanding Neural Models in NLP , 2015, NAACL.
[28] Lysandre Debut,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[29] Jasmijn Bastings,et al. The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? , 2020, BLACKBOXNLP.
[30] Sameer Singh,et al. Gradient-based Analysis of NLP Models is Manipulable , 2020, FINDINGS.
[31] Marcello Federico,et al. Report on the 10th IWSLT evaluation campaign , 2013, IWSLT.
[32] Yoav Goldberg,et al. Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? , 2020, ACL.
[33] Hermann Ney,et al. Improved Statistical Alignment Models , 2000, ACL.
[34] Hinrich Schütze,et al. Evaluating neural network explanation methods using hybrid documents and morphological agreement , 2018 .
[35] Byron C. Wallace,et al. Learning to Faithfully Rationalize by Construction , 2020, ACL.
[36] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[37] Tommi S. Jaakkola,et al. A causal framework for explaining the predictions of black-box sequence-to-sequence models , 2017, EMNLP.
[38] Jacob Cohen. A Coefficient of Agreement for Nominal Scales , 1960 .
[39] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[40] Yuval Pinter,et al. Attention is not not Explanation , 2019, EMNLP.
[41] Yang Liu,et al. On Identifiability in Transformers , 2020, ICLR.
[42] Kentaro Inui,et al. Attention is Not Only a Weight: Analyzing Transformers with Vector Norms , 2020, EMNLP.
[43] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[44] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[45] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[46] Dumitru Erhan,et al. A Benchmark for Interpretability Methods in Deep Neural Networks , 2018, NeurIPS.
[47] Mihaela van der Schaar,et al. INVASE: Instance-wise Variable Selection using Neural Networks , 2018, ICLR.
[48] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[49] Misha Denil,et al. Extraction of Salient Sentences from Labelled Documents , 2014, ArXiv.
[50] Abubakar Abid,et al. Interpretation of Neural Networks is Fragile , 2017, AAAI.