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Comparing Automatic and Human Evaluation of Local Explanations for Text Classification
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Text classification models are becoming increasingly complex and opaque, however for many applications it is essential that the models are interpretable. Recently, a variety of approaches have been proposed for generating local explanations. While robust evaluations are needed to drive further progress, so far it is unclear which evaluation approaches are suitable. This paper is a first step towards more robust evaluations of local explanations. We evaluate a variety of local explanationdoi:10.18653/v1/n18-1097 dblp:conf/naacl/Nguyen18 fatcat:tcomp6cpbjgs3pwdcjq4gf7uta