@inproceedings{tang-surdeanu-2021-interpretability,
title = "Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractorwith an Explanation Decoder",
author = "Tang, Zheng and
Surdeanu, Mihai",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tang-surdeanu-2021-interpretability">
<titleInfo>
<title>Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractorwith an Explanation Decoder</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zheng</namePart>
<namePart type="family">Tang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mihai</namePart>
<namePart type="family">Surdeanu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-jun</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Trustworthy Natural Language Processing</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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>
<identifier type="citekey">tang-surdeanu-2021-interpretability</identifier>
<identifier type="doi">10.18653/v1/2021.trustnlp-1.1</identifier>
<location>
<url>https://aclanthology.org/2021.trustnlp-1.1</url>
</location>
<part>
<date>2021-jun</date>
<extent unit="page">
<start>1</start>
<end>7</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractorwith an Explanation Decoder
%A Tang, Zheng
%A Surdeanu, Mihai
%S Proceedings of the First Workshop on Trustworthy Natural Language Processing
%D 2021
%8 jun
%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