Fetching the paper…
Reading the bibliography…
Current methods for Black-Box NLP interpretability, like LIME or SHAP, are based on altering the text to interpret by removing words and modeling the Black-Box response.
Dimopoulos, Y., Bourret, P., Lek, S.: Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Processing Letters 2
1995
Earlier work this paper cites.
Bird, S., Klein, E., Loper, E.: Natural language processing with Python: analyzing text with the natural language toolkit. " O’Reilly Media, Inc." (2009)
2009
Earlier work this paper cites.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12
2011
Earlier work this paper cites.
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 427–436 (2015)
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Arras, L., Horn, F., Montavon, G.: Explaining predictions of non-linear classifiers in nlp. ACL 2016 p. 1 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Lei, T., Barzilay, R., Jaakkola, T.: Rationalizing neural predictions. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. pp. 107–117 (2016)
2016
Earlier work this paper cites.
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. pp. 2383–2392 (2016)
2016
Earlier work this paper cites.
Ribeiro, M.T., Singh, S., Guestrin, C.: " why should i trust you?" explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp. 1135–1144 (2016)
2016
Cited alongside, same era.
2017
Cited alongside, same era.
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in neural information processing systems, pp. 4765–4774 (2017)
2017
Cited alongside, same era.
Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1765–1773 (2017)
2017
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
2020
Closest in time.
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: Advances in Neural Information Processing Systems. pp. 7167–7177 (2018)
2018
Cited alongside, same era.
Rajpurkar, P., Jia, R., Liang, P.: Know what you don’t know: Unanswerable questions for squad. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). pp. 784–789 (2018)
2018
Cited alongside, same era.
Chang, S., Zhang, Y., Yu, M., Jaakkola, T.: A game theoretic approach to class-wise selective rationalization. In: Advances in Neural Information Processing Systems. pp. 10055–10065 (2019)
2019
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186 (2019)
2019
Cited alongside, same era.
Lampridis, O., Guidotti, R., Ruggieri, S.: Explaining sentiment classification with synthetic exemplars and counter-exemplars. In: International Conference on Discovery Science. pp. 357–373. Springer (2020)
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2021
Closest in time.
Bibal, A., Cardon, R., Alfter, D., Wilkens, R., Wang, X., François, T., Watrin, P.: Is attention explanation? an introduction to the debate. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 3889–3900 (2022)
2022
Closest in time.