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        <title>Dual-Channel Span for Aspect Sentiment Triplet Extraction</title>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Aspect Sentiment Triplet Extraction (ASTE) is one of the compound tasks of fine-grained aspect-based sentiment analysis (ABSA), aiming at extracting the triplets of aspect terms, corresponding opinion terms and the associated sentiment orientation. Recent efforts in exploiting span-level semantic interaction shown superior performance on ASTE task. However, most of the existing span-based approaches suffer from enumerating all possible spans, since it can introduce too much noise in sentiment triplet extraction. To ease this burden, we propose a dual-channel span generation method to coherently constrain the search space of span candidates. Specifically, we leverage the syntactic relations among aspect/opinion terms and the associated part-of-speech characteristics in those terms to generate span candidates, which reduces span enumeration by nearly half. Besides, feature representations are learned from syntactic and part-of-speech correlation among terms, which renders span representation fruitful linguistic information. Extensive experiments on two versions of public datasets demonstrate both the effectiveness of our design and the superiority on ASTE/ATE/OTE tasks.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.17</identifier>
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%0 Conference Proceedings
%T Dual-Channel Span for Aspect Sentiment Triplet Extraction
%A Li, Pan
%A Li, Ping
%A Zhang, Kai
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-dual-channel
%X Aspect Sentiment Triplet Extraction (ASTE) is one of the compound tasks of fine-grained aspect-based sentiment analysis (ABSA), aiming at extracting the triplets of aspect terms, corresponding opinion terms and the associated sentiment orientation. Recent efforts in exploiting span-level semantic interaction shown superior performance on ASTE task. However, most of the existing span-based approaches suffer from enumerating all possible spans, since it can introduce too much noise in sentiment triplet extraction. To ease this burden, we propose a dual-channel span generation method to coherently constrain the search space of span candidates. Specifically, we leverage the syntactic relations among aspect/opinion terms and the associated part-of-speech characteristics in those terms to generate span candidates, which reduces span enumeration by nearly half. Besides, feature representations are learned from syntactic and part-of-speech correlation among terms, which renders span representation fruitful linguistic information. Extensive experiments on two versions of public datasets demonstrate both the effectiveness of our design and the superiority on ASTE/ATE/OTE tasks.
%R 10.18653/v1/2023.emnlp-main.17
%U https://aclanthology.org/2023.emnlp-main.17
%U https://doi.org/10.18653/v1/2023.emnlp-main.17
%P 248-261

Markdown (Informal)

[Dual-Channel Span for Aspect Sentiment Triplet Extraction](https://aclanthology.org/2023.emnlp-main.17) (Li et al., EMNLP 2023)

• Dual-Channel Span for Aspect Sentiment Triplet Extraction (Li et al., EMNLP 2023)

ACL

• Pan Li, Ping Li, and Kai Zhang. 2023. Dual-Channel Span for Aspect Sentiment Triplet Extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 248–261, Singapore. Association for Computational Linguistics.