@inproceedings{7b883d6add5f4d53879ace0b1d47aa08,
title = "Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractor with an Explanation Decoder",
abstract = "We introduce a method that transforms a rulebased 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.",
author = "Zheng Tang and Mihai Surdeanu",
note = "Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; 1st Workshop on Trustworthy Natural Language Processing, TrustNLP 2021 ; Conference date: 10-06-2021",
year = "2021",
language = "English (US)",
series = "TrustNLP 2021 - 1st Workshop on Trustworthy Natural Language Processing, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "1--7",
editor = "Yada Pruksachatkun and Anil Ramakrishna and Kai-Wei Chang and Satyapriya Krishna and Jwala Dhamala and Tanaya Guha and Xiang Ren",
booktitle = "TrustNLP 2021 - 1st Workshop on Trustworthy Natural Language Processing, Proceedings of the Workshop",
}