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A common approach to explaining NLP models is to use importance measures that express which tokens are important for a prediction.
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 · 1907
Earlier work this paper cites.
Attention Interpretability Across NLP Tasks
Vashishth, S., Upadhyay, S., Tomar, G. S., and Faruqui, M · 1909
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”What is relevant in a text document?”: An interpretable machine learning approach
Arras, L., Horn, F., Montavon, G., Müller, K.-R., and Samek, W · 1932
Earlier work this paper cites.
An Improved Bonferroni Procedure for Multiple Tests of Significance
Simes, R. J · 1986
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Statistical Methods for Research Workers
Fisher, R. A · 1992
Earlier work this paper cites.
Bootstrap Methods and Their Application
Buckland, S. T., Davison, A. C., and Hinkley, D. V · 1998
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ROUGE: A Package for Automatic Evaluation of Summaries
Lin, C.-Y · 2004
Earlier work this paper cites.
Automatically Constructing a Corpus of Sentential Paraphrases
Dolan, W. B. and Brockett, C · 2005
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The PASCAL Recognising Textual Entailment Challenge
Dagan, I., Glickman, O., and Magnini, B · 2006
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HotFlip: White-Box Adversarial Examples for Text Classification
Ebrahimi, J., Rao, A., Lowd, D., and Dou, D · 2006
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Backpropagated Gradient Representations for Anomaly Detection
Kwon, G., Prabhushankar, M., Temel, D., and AlRegib, G · 2007
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How to explain individual classification decisions
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., and Müller, K. R · 2010
Earlier work this paper cites.
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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An introduction to bootstrap methods with applications to R
Michael R. Chernick and LaBudde, R. A · 2011
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Parsing with compositional vector grammars
Socher, R., Bauer, J., Manning, C. D., and Ng, A. Y · 2013
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A large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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MIMIC-III, a freely accessible critical care database
Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L.-w. H. W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., and Mark, R. G · 2016
Earlier work this paper cites.
Investigating the influence of noise and distractors on the interpretation of neural networks
Kindermans, P.-J., Schütt, K., Müller, K.-R., and Dähne, S · 2016
Earlier work this paper cites.
Visualizing and Understanding Neural Models in NLP
Li, J., Chen, X., Hovy, E., and Jurafsky, D · 2016
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SQuad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
Earlier work this paper cites.
Evaluating the Visualization of What a Deep Neural Network Has Learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., and Muller, K.-R · 2016
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Towards AI-complete question answering: A set of prerequisite toy tasks
Weston, J., Bordes, A., Chopra, S., Rush, A. M., Van Merriënboer, B., Joulin, A., and Mikolov, T · 2016
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Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F. and Kim, B · 2017
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Accountability of AI Under the Law: The Role of Explanation
Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S. J., O’Brien, D., Shieber, S., Waldo, J., Weinberger, D., and Wood, A · 2017
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European union regulations on algorithmic decision making and a ”right to explanation”
Goodman, B. and Flaxman, S · 2017
Cited alongside, same era.
First Quora Dataset Release: Question Pairs, 2017
Iyer, S., Dandekar, N., and Csernai, K · 2017
Cited alongside, same era.
KS(conf): A Light-Weight Test if a Multiclass Classifier Operates Outside of Its Specifications
Sun, R. and Lampert, C. H · 2020
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CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations
Arras, L., Osman, A., and Samek, W · 2021
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The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations
Hase, P., Xie, H., and Bansal, M · 2021
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Is Sparse Attention more Interpretable?
Meister, C., Lazov, S., Augenstein, I., and Cotterell, R · 2021
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Measuring and improving faithfulness of attention in neural machine translation
Moradi, P., Kambhatla, N., and Sarkar, A · 2021
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Sundararajan, M., Taly, A., and Yan, Q · 2017
Cited alongside, same era.
Did the model understand the question?
Mudrakarta, P. K., Taly, A., Sundararajan, M., and Dhamdhere, K · 2018
Cited alongside, same era.
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Williams, A., Nangia, N., and Bowman, S · 2018
Cited alongside, same era.
Explainable Machine Learning in Deployment
Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M. F., and Eckersley, P · 2019
Cited alongside, same era.
Boolq: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M. W., Kwiatkowski, T., Collins, M., and Toutanova, K · 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
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks
Hooker, S., Erhan, D., Kindermans, P.-J. J., and Kim, B · 2019
Cited alongside, same era.
Explaining NLP Models via Minimal Contrastive Editing (MiCE)
Ross, A., Marasović, A., and Peters, M · 2021
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Rationales for Sequential Predictions
Vafa, K., Deng, Y., Blei, D., and Rush, A · 2021
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Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
Wu, T., Ribeiro, M. T., Heer, J., and Weld, D · 2021
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Generalized Out-of-Distribution Detection: A Survey
Yang, J., Zhou, K., Li, Y., and Liu, Z · 2021
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“Will You Find These Shortcuts?” A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification
Bastings, J., Ebert, S., Zablotskaia, P., Sandholm, A., and Filippova, K · 2022
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$p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations
Dziedzic, A., Rabanser, S., Yaghini, M., Ale, A., Erdogdu, M. A., and Papernot, N · 2022
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Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining
Madsen, A., Meade, N., Adlakha, V., and Reddy, S · 2022
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A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks
Matan, H., Frostig, T., Heller, R., and Soudry, D · 2022
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Interpreting Language Models with Contrastive Explanations
Yin, K. and Neubig, G · 2022
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Zaman, K. and Belinkov, Y · 2022
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GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations
Amara, K., El-Assady, M., and Ying, R · 2023
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Should You Mask 15% in Masked Language Modeling?
Wettig, A., Gao, T., Zhong, Z., and Chen, D · 2023
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Applications of transformer-based language models in bioinformatics: a survey
Zhang, S., Fan, R., Liu, Y., Chen, S., Liu, Q., and Zeng, W · 2023
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The Solvability of Interpretability Evaluation Metrics
Zhou, Y. and Shah, J · 2023
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Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., and Roth, D · 2024
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Generative Representational Instruction Tuning
Muennighoff, N., Su, H., Wang, L., Yang, N., Wei, F., Yu, T., Singh, A., and Kiela, D · 2024
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