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    <abstract>Although neural models have shown strong performance in datasets such as SNLI, they lack the ability to generalize out-of-distribution (OOD). In this work, we formulate a few-shot learning setup and examine the effects of natural language explanations on OOD generalization. We leverage the templates in the HANS dataset and construct templated natural language explanations for each template. Although generated explanations show competitive BLEU scores against ground truth explanations, they fail to improve prediction performance. We further show that generated explanations often hallucinate information and miss key elements that indicate the label.</abstract>
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%0 Conference Proceedings
%T Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI
%A Zhou, Yangqiaoyu
%A Tan, Chenhao
%Y Sedoc, João
%Y Rogers, Anna
%Y Rumshisky, Anna
%Y Tafreshi, Shabnam
%S Proceedings of the Second Workshop on Insights from Negative Results in NLP
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F zhou-tan-2021-investigating
%X Although neural models have shown strong performance in datasets such as SNLI, they lack the ability to generalize out-of-distribution (OOD). In this work, we formulate a few-shot learning setup and examine the effects of natural language explanations on OOD generalization. We leverage the templates in the HANS dataset and construct templated natural language explanations for each template. Although generated explanations show competitive BLEU scores against ground truth explanations, they fail to improve prediction performance. We further show that generated explanations often hallucinate information and miss key elements that indicate the label.
%R 10.18653/v1/2021.insights-1.17
%U https://aclanthology.org/2021.insights-1.17/
%U https://doi.org/10.18653/v1/2021.insights-1.17
%P 117-124

Markdown (Informal)

[Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI](https://aclanthology.org/2021.insights-1.17/) (Zhou & Tan, insights 2021)

• Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI (Zhou & Tan, insights 2021)

ACL

• Yangqiaoyu Zhou and Chenhao Tan. 2021. Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI. In Proceedings of the Second Workshop on Insights from Negative Results in NLP, pages 117–124, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.