@inproceedings{zhou-tan-2021-investigating,
    title = "Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot {NLI}",
    author = "Zhou, Yangqiaoyu  and
      Tan, Chenhao",
    editor = "Sedoc, Jo{\~a}o  and
      Rogers, Anna  and
      Rumshisky, Anna  and
      Tafreshi, Shabnam",
    booktitle = "Proceedings of the Second Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.insights-1.17/",
    doi = "10.18653/v1/2021.insights-1.17",
    pages = "117--124",
    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."
}