Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision

@inproceedings{Brahman2020LearningTR,
  title={Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision},
  author={Faeze Brahman and Vered Shwartz and Rachel Rudinger and Yejin Choi},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2020}
}
The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such rationale generation models, to date, have been trained on dataset-specific crowdsourced rationales, but this approach is costly and is not generalizable to new tasks and domains. In this paper, we investigate the extent to which neural models can reason about natural language rationales that explain model predictions… 

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