@inproceedings{tang-surdeanu-2021-interpretability,
    title = "Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractorwith an Explanation Decoder",
    author = "Tang, Zheng  and
      Surdeanu, Mihai",
    editor = "Pruksachatkun, Yada  and
      Ramakrishna, Anil  and
      Chang, Kai-Wei  and
      Krishna, Satyapriya  and
      Dhamala, Jwala  and
      Guha, Tanaya  and
      Ren, Xiang",
    booktitle = "Proceedings of the First Workshop on Trustworthy Natural Language Processing",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.trustnlp-1.1",
    doi = "10.18653/v1/2021.trustnlp-1.1",
    pages = "1--7",
    abstract = "We introduce a method that transforms a rule-based relation extraction (RE) classifier into a neural one such that both interpretability and performance are achieved. Our approach jointly trains a RE classifier with a decoder that generates explanations for these extractions, using as sole supervision a set of rules that match these relations. Our evaluation on the TACRED dataset shows that our neural RE classifier outperforms the rule-based one we started from by 9 F1 points; our decoder generates explanations with a high BLEU score of over 90{\%}; and, the joint learning improves the performance of both the classifier and decoder.",
}