e-SNLI: Natural Language Inference with Natural Language Explanations

NeurIPS 2018 Oana-Maria CamburuTim RocktäschelThomas LukasiewiczPhil Blunsom

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... (read more)

PDF Abstract NeurIPS 2018 PDF NeurIPS 2018 Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet