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E-SNLI: Natural language inference with natural language explanations

Camburu, OM; Rocktäschel, T; Lukasiewicz, T; Blunsom, P; (2018) E-SNLI: Natural language inference with natural language explanations. In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and Cesa-Bianchi, N and Garnett, R, (eds.) Advances in Neural Information Processing Systems 31. (pp. pp. 9539-9549). NIPS Proceedings: Montréal, Canada. Green open access

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Abstract

In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from human-provided explanations at train time. In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the entailment relations. We further implement models that incorporate these explanations into their training process and output them at test time. We show how our corpus of explanations, which we call e-SNLI, can be used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets. Our dataset 1 thus opens up a range of research directions for using natural language explanations, both for improving models and for asserting their trust.

Type: Proceedings paper
Title: E-SNLI: Natural language inference with natural language explanations
Event: Neural Information Processing Systems 2018
Open access status: An open access version is available from UCL Discovery
Publisher version: https://papers.nips.cc/paper/8163-e-snli-natural-l...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10074410
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