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Selective rationalization improves neural network interpretability by identifying a small subset of input features -- the rationale -- that best explains or supports the prediction.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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An efficient explanation of individual classifications using game theory
Kononenko, I. et al · 2010
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Learning word vectors for sentiment analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C · 2011
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Learning attitudes and attributes from multi-aspect reviews
McAuley, J., Leskovec, J., and Jurafsky, D · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C · 2014
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Rationalizing neural predictions
Lei, T., Barzilay, R., and Jaakkola, T · 2016
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Visualizing and understanding neural models in NLP
Li, J., Chen, X., Hovy, E., and Jurafsky, D · 2016
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Why should I trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Inferring and executing programs for visual reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Hoffman, J., Fei-Fei, L., Lawrence Zitnick, C., and Girshick, R · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Don’t just assume; look and answer: Overcoming priors for visual question answering
Agrawal, A., Batra, D., Parikh, D., and Kembhavi, A · 2018
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Towards robust interpretability with self-explaining neural networks
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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On adversarial removal of hypothesis-only bias in natural language inference
Belinkov, Y., Poliak, A., Shieber, S. M., Van Durme, B., and Rush, A. M · 2019
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A game theoretic approach to class-wise selective rationalization
Chang, S., Zhang, Y., Yu, M., and Jaakkola, T · 2019
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Clark, C., Yatskar, M., and Zettlemoyer, L · 2019
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Unlearn dataset bias in natural language inference by fitting the residual
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Alvarez-Melis, D. and Jaakkola, T. S · 2018
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Deriving machine attention from human rationales
Bao, Y., Chang, S., Yu, M., and Barzilay, R · 2018
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Generative question answering: Learning to answer the whole question
Lewis, M. and Fan, A · 2018
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Overcoming language priors in visual question answering with adversarial regularization
Ramakrishnan, S., Agrawal, A., and Lee, S · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Welbl, J., Stenetorp, P., and Riedel, S · 2018
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Learning corresponded rationales for text matching
Yu, M., Chang, S., and Jaakkola, T. S · 2018
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Learning to compose neural networks for question answering
Andreas, J., Rohrbach, M., Darrell, T., and Klein, D
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He, H., Zha, S., and Wang, H · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
McCoy, T., Pavlick, E., and Linzen, T · 2019
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Compositional questions do not necessitate multi-hop reasoning
Min, S., Wallace, E., Singh, S., Gardner, M., Hajishirzi, H., and Zettlemoyer, L · 2019
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Probing neural network comprehension of natural language arguments
Niven, T. and Kao, H.-Y · 2019
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Do multi-hop readers dream of reasoning chains?
Wang, H., Yu, M., Guo, X., Das, R., Xiong, W., and Gao, T · 2019
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Rethinking cooperative rationalization: Introspective extraction and complement control
Yu, M., Chang, S., Zhang, Y., and Jaakkola, T. S · 2019
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