@inproceedings{murty-etal-2020-expbert,
    title = "{E}xp{BERT}: Representation Engineering with Natural Language Explanations",
    author = "Murty, Shikhar  and
      Koh, Pang Wei  and
      Liang, Percy",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.acl-main.190/",
    doi = "10.18653/v1/2020.acl-main.190",
    pages = "2106--2113",
    abstract = "Suppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text. In this paper, we allow model developers to specify these types of inductive biases as natural language explanations. We use BERT fine-tuned on MultiNLI to ``interpret'' these explanations with respect to the input sentence, producing explanation-guided representations of the input. Across three relation extraction tasks, our method, ExpBERT, matches a BERT baseline but with 3{--}20x less labeled data and improves on the baseline by 3{--}10 F1 points with the same amount of labeled data."
}