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        <title>A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction</title>
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    <abstract>Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity. Existing methods are poor at detecting complicated relations between aspects and opinions as well as classifying multiple sentiment polarities in a sentence. Detecting unclear boundaries of multi-word aspects and opinions is also a challenge. In this paper, we propose a Multi-Task Dual-Tree Network (MTDTN) to address these issues. We employ a constituency tree and a modified dependency tree in two sub-tasks of Aspect Opinion Co-Extraction (AOCE) and ASTE, respectively. To enhance the information interaction between the two sub-tasks, we further design a Transition-Based Inference Strategy (TBIS) that transfers the boundary information from tags of AOCE to ASTE through a transition matrix. Extensive experiments are conducted on four popular datasets, and the results show the effectiveness of our model.</abstract>
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%0 Conference Proceedings
%T A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction
%A Zhao, Yichun
%A Meng, Kui
%A Liu, Gongshen
%A Du, Jintao
%A Zhu, Huijia
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F zhao-etal-2022-multi
%X Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity. Existing methods are poor at detecting complicated relations between aspects and opinions as well as classifying multiple sentiment polarities in a sentence. Detecting unclear boundaries of multi-word aspects and opinions is also a challenge. In this paper, we propose a Multi-Task Dual-Tree Network (MTDTN) to address these issues. We employ a constituency tree and a modified dependency tree in two sub-tasks of Aspect Opinion Co-Extraction (AOCE) and ASTE, respectively. To enhance the information interaction between the two sub-tasks, we further design a Transition-Based Inference Strategy (TBIS) that transfers the boundary information from tags of AOCE to ASTE through a transition matrix. Extensive experiments are conducted on four popular datasets, and the results show the effectiveness of our model.
%U https://aclanthology.org/2022.coling-1.616
%P 7065-7074

Markdown (Informal)

[A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction](https://aclanthology.org/2022.coling-1.616) (Zhao et al., COLING 2022)

• A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction (Zhao et al., COLING 2022)

ACL

• Yichun Zhao, Kui Meng, Gongshen Liu, Jintao Du, and Huijia Zhu. 2022. A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 7065–7074, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.