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e-SNLI: Natural language inference with natural language explanations

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 out...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
ORCID:
0000-0002-7644-1668
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
Publisher:
Neural Information Processing Systems Publisher's website
Volume:
31
Publication date:
2018-01-01
Acceptance date:
2018-09-05
Pubs id:
pubs:935179
URN:
uri:2ba47384-691f-4fab-b5a3-9770278888d3
UUID:
uuid:2ba47384-691f-4fab-b5a3-9770278888d3
Local pid:
pubs:935179

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