<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="murty-etal-2020-expbert">
    <titleInfo>
        <title>ExpBERT: Representation Engineering with Natural Language Explanations</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Shikhar</namePart>
        <namePart type="family">Murty</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Pang</namePart>
        <namePart type="given">Wei</namePart>
        <namePart type="family">Koh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Percy</namePart>
        <namePart type="family">Liang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joyce</namePart>
            <namePart type="family">Chai</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Natalie</namePart>
            <namePart type="family">Schluter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">murty-etal-2020-expbert</identifier>
    <identifier type="doi">10.18653/v1/2020.acl-main.190</identifier>
    <location>
        <url>https://aclanthology.org/2020.acl-main.190/</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>2106</start>
            <end>2113</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T ExpBERT: Representation Engineering with Natural Language Explanations
%A Murty, Shikhar
%A Koh, Pang Wei
%A Liang, Percy
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F murty-etal-2020-expbert
%X 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.
%R 10.18653/v1/2020.acl-main.190
%U https://aclanthology.org/2020.acl-main.190/
%U https://doi.org/10.18653/v1/2020.acl-main.190
%P 2106-2113

Markdown (Informal)

[ExpBERT: Representation Engineering with Natural Language Explanations](https://aclanthology.org/2020.acl-main.190/) (Murty et al., ACL 2020)

• ExpBERT: Representation Engineering with Natural Language Explanations (Murty et al., ACL 2020)

ACL

• Shikhar Murty, Pang Wei Koh, and Percy Liang. 2020. ExpBERT: Representation Engineering with Natural Language Explanations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2106–2113, Online. Association for Computational Linguistics.