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        <title>A Multi-task Learning Framework for Opinion Triplet Extraction</title>
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    <name type="personal">
        <namePart type="given">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Qiuchi</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
        <namePart type="given">Dawei</namePart>
        <namePart type="family">Song</namePart>
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    <name type="personal">
        <namePart type="given">Benyou</namePart>
        <namePart type="family">Wang</namePart>
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        <dateIssued>2020-11</dateIssued>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2020</title>
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            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-of-the-art approaches.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.72</identifier>
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        <url>https://aclanthology.org/2020.findings-emnlp.72</url>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T A Multi-task Learning Framework for Opinion Triplet Extraction
%A Zhang, Chen
%A Li, Qiuchi
%A Song, Dawei
%A Wang, Benyou
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-multi-task
%X The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-of-the-art approaches.
%R 10.18653/v1/2020.findings-emnlp.72
%U https://aclanthology.org/2020.findings-emnlp.72
%U https://doi.org/10.18653/v1/2020.findings-emnlp.72
%P 819-828

Markdown (Informal)

[A Multi-task Learning Framework for Opinion Triplet Extraction](https://aclanthology.org/2020.findings-emnlp.72) (Zhang et al., Findings 2020)

• A Multi-task Learning Framework for Opinion Triplet Extraction (Zhang et al., Findings 2020)

ACL

• Chen Zhang, Qiuchi Li, Dawei Song, and Benyou Wang. 2020. A Multi-task Learning Framework for Opinion Triplet Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 819–828, Online. Association for Computational Linguistics.