<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhai-etal-2022-com">
    <titleInfo>
        <title>COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zepeng</namePart>
        <namePart type="family">Zhai</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hao</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Fangxiang</namePart>
        <namePart type="family">Feng</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ruifan</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiaojie</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Abu Dhabi, United Arab Emirates</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, which was recently formalized as an effective machine reading comprehension (MRC) based framework. However, when facing multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms. In this paper, we propose a novel COntext-Masked MRC (COM-MRC) framework for ASTE. Our COM-MRC framework comprises three closely-related components: a context augmentation strategy, a discriminative model, and an inference method. Specifically, a context augmentation strategy is designed by enumerating all masked contexts for each aspect term. The discriminative model comprises four modules, i.e., aspect and opinion extraction modules, sentiment classification and aspect detection modules. In addition, a two-stage inference method first extracts all aspects and then identifies their opinions and sentiment through iteratively masking the aspects. Extensive experimental results on benchmark datasets show the effectiveness of our proposed COM-MRC framework, which outperforms state-of-the-art methods consistently.</abstract>
    <identifier type="citekey">zhai-etal-2022-com</identifier>
    <location>
        <url>https://aclanthology.org/2022.emnlp-main.212</url>
    </location>
    <part>
        <date>2022-12</date>
        <extent unit="page">
            <start>3230</start>
            <end>3241</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction
%A Zhai, Zepeng
%A Chen, Hao
%A Feng, Fangxiang
%A Li, Ruifan
%A Wang, Xiaojie
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhai-etal-2022-com
%X Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, which was recently formalized as an effective machine reading comprehension (MRC) based framework. However, when facing multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms. In this paper, we propose a novel COntext-Masked MRC (COM-MRC) framework for ASTE. Our COM-MRC framework comprises three closely-related components: a context augmentation strategy, a discriminative model, and an inference method. Specifically, a context augmentation strategy is designed by enumerating all masked contexts for each aspect term. The discriminative model comprises four modules, i.e., aspect and opinion extraction modules, sentiment classification and aspect detection modules. In addition, a two-stage inference method first extracts all aspects and then identifies their opinions and sentiment through iteratively masking the aspects. Extensive experimental results on benchmark datasets show the effectiveness of our proposed COM-MRC framework, which outperforms state-of-the-art methods consistently.
%U https://aclanthology.org/2022.emnlp-main.212
%P 3230-3241

Markdown (Informal)

[COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction](https://aclanthology.org/2022.emnlp-main.212) (Zhai et al., EMNLP 2022)

• COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction (Zhai et al., EMNLP 2022)

ACL

• Zepeng Zhai, Hao Chen, Fangxiang Feng, Ruifan Li, and Xiaojie Wang. 2022. COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3230–3241, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.