Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction

Hao Chen, Zepeng Zhai, Fangxiang Feng, Ruifan Li, Xiaojie Wang


Abstract
Aspect Sentiment Triplet Extraction (ASTE) is an emerging sentiment analysis task. Most of the existing studies focus on devising a new tagging scheme that enables the model to extract the sentiment triplets in an end-to-end fashion. However, these methods ignore the relations between words for ASTE task. In this paper, we propose an Enhanced Multi-Channel Graph Convolutional Network model (EMC-GCN) to fully utilize the relations between words. Specifically, we first define ten types of relations for ASTE task, and then adopt a biaffine attention module to embed these relations as an adjacent tensor between words in a sentence. After that, our EMC-GCN transforms the sentence into a multi-channel graph by treating words and the relation adjacent tensor as nodes and edges, respectively. Thus, relation-aware node representations can be learnt. Furthermore, we consider diverse linguistic features to enhance our EMC-GCN model. Finally, we design an effective refining strategy on EMC-GCN for word-pair representation refinement, which considers the implicit results of aspect and opinion extraction when determining whether word pairs match or not. Extensive experimental results on the benchmark datasets demonstrate that the effectiveness and robustness of our proposed model, which outperforms state-of-the-art methods significantly.
Anthology ID:
2022.acl-long.212
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2974–2985
Language:
URL:
https://aclanthology.org/2022.acl-long.212
DOI:
10.18653/v1/2022.acl-long.212
Bibkey:
Cite (ACL):
Hao Chen, Zepeng Zhai, Fangxiang Feng, Ruifan Li, and Xiaojie Wang. 2022. Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2974–2985, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction (Chen et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.212.pdf
Software:
 2022.acl-long.212.software.zip
Code
 ccchenhao997/emcgcn-aste