- Abstract:
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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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
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(pdf, 226.5KB)
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- Publisher:
- Neural Information Processing Systems Publisher's website
- Volume:
- 31
- Publication date:
- 2018-01-01
- Acceptance date:
- 2018-09-05
- Pubs id:
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pubs:935179
- URN:
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uri:2ba47384-691f-4fab-b5a3-9770278888d3
- UUID:
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uuid:2ba47384-691f-4fab-b5a3-9770278888d3
- Local pid:
- pubs:935179
- Copyright date:
- 2018
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from Neural Information Processing Systems at: https://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations
Conference item
e-SNLI: Natural language inference with natural language explanations
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