<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="klein-etal-2022-opinion">
    <titleInfo>
        <title>Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ayal</namePart>
        <namePart type="family">Klein</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Oren</namePart>
        <namePart type="family">Pereg</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Korat</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Vasudev</namePart>
        <namePart type="family">Lal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Moshe</namePart>
        <namePart type="family">Wasserblat</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ido</namePart>
        <namePart type="family">Dagan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment &amp; Social Media Analysis</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dublin, Ireland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Domain adaptation methods often exploit domain-transferable input features, a.k.a. pivots. The task of Aspect and Opinion Term Extraction presents a special challenge for domain transfer: while opinion terms largely transfer across domains, aspects change drastically from one domain to another (e.g. from restaurants to laptops). In this paper, we investigate and establish empirically a prior conjecture, which suggests that the linguistic relations connecting opinion terms to their aspects transfer well across domains and therefore can be leveraged for cross-domain aspect term extraction. We present several analyses supporting this conjecture, via experiments with four linguistic dependency formalisms to represent relation patterns. Subsequently, we present an aspect term extraction method that drives models to consider opinion–aspect relations via explicit multitask objectives. This method provides significant performance gains, even on top of a prior state-of-the-art linguistically-informed model, which are shown in analysis to stem from the relational pivoting signal.</abstract>
    <identifier type="citekey">klein-etal-2022-opinion</identifier>
    <identifier type="doi">10.18653/v1/2022.wassa-1.11</identifier>
    <location>
        <url>https://aclanthology.org/2022.wassa-1.11</url>
    </location>
    <part>
        <date>2022-05</date>
        <extent unit="page">
            <start>104</start>
            <end>112</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction
%A Klein, Ayal
%A Pereg, Oren
%A Korat, Daniel
%A Lal, Vasudev
%A Wasserblat, Moshe
%A Dagan, Ido
%S Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F klein-etal-2022-opinion
%X Domain adaptation methods often exploit domain-transferable input features, a.k.a. pivots. The task of Aspect and Opinion Term Extraction presents a special challenge for domain transfer: while opinion terms largely transfer across domains, aspects change drastically from one domain to another (e.g. from restaurants to laptops). In this paper, we investigate and establish empirically a prior conjecture, which suggests that the linguistic relations connecting opinion terms to their aspects transfer well across domains and therefore can be leveraged for cross-domain aspect term extraction. We present several analyses supporting this conjecture, via experiments with four linguistic dependency formalisms to represent relation patterns. Subsequently, we present an aspect term extraction method that drives models to consider opinion–aspect relations via explicit multitask objectives. This method provides significant performance gains, even on top of a prior state-of-the-art linguistically-informed model, which are shown in analysis to stem from the relational pivoting signal.
%R 10.18653/v1/2022.wassa-1.11
%U https://aclanthology.org/2022.wassa-1.11
%U https://doi.org/10.18653/v1/2022.wassa-1.11
%P 104-112

Markdown (Informal)

[Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction](https://aclanthology.org/2022.wassa-1.11) (Klein et al., WASSA 2022)

• Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction (Klein et al., WASSA 2022)

ACL

• Ayal Klein, Oren Pereg, Daniel Korat, Vasudev Lal, Moshe Wasserblat, and Ido Dagan. 2022. Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 104–112, Dublin, Ireland. Association for Computational Linguistics.