@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractorwith an Explanation Decoder
%A Tang, Zheng
%A Surdeanu, Mihai
%Y Pruksachatkun, Yada
%Y Ramakrishna, Anil
%Y Chang, Kai-Wei
%Y Krishna, Satyapriya
%Y Dhamala, Jwala
%Y Guha, Tanaya
%Y Ren, Xiang
%S Proceedings of the First Workshop on Trustworthy Natural Language Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F tang-surdeanu-2021-interpretability
%X 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.
%R 10.18653/v1/2021.trustnlp-1.1
%U https://aclanthology.org/2021.trustnlp-1.1
%U https://doi.org/10.18653/v1/2021.trustnlp-1.1
%P 1-7
Markdown (Informal)
[Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractorwith an Explanation Decoder](https://aclanthology.org/2021.trustnlp-1.1) (Tang & Surdeanu, TrustNLP 2021)
ACL