Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis

Chenggong Gong, Jianfei Yu, Rui Xia


Abstract
The supervised models for aspect-based sentiment analysis (ABSA) rely heavily on labeled data. However, fine-grained labeled data are scarce for the ABSA task. To alleviate the dependence on labeled data, prior works mainly focused on feature-based adaptation, which used the domain-shared knowledge to construct auxiliary tasks or domain adversarial learning to bridge the gap between domains, while ignored the attribute of instance-based adaptation. To resolve this limitation, we propose an end-to-end framework to jointly perform feature and instance based adaptation for the ABSA task in this paper. Based on BERT, we learn domain-invariant feature representations by using part-of-speech features and syntactic dependency relations to construct auxiliary tasks, and jointly perform word-level instance weighting in the framework of sequence labeling. Experiment results on four benchmarks show that the proposed method can achieve significant improvements in comparison with the state-of-the-arts in both tasks of cross-domain End2End ABSA and cross-domain aspect extraction.
Anthology ID:
2020.emnlp-main.572
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7035–7045
Language:
URL:
https://aclanthology.org/2020.emnlp-main.572
DOI:
10.18653/v1/2020.emnlp-main.572
Bibkey:
Cite (ACL):
Chenggong Gong, Jianfei Yu, and Rui Xia. 2020. Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7035–7045, Online. Association for Computational Linguistics.
Cite (Informal):
Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (Gong et al., EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.572.pdf
Video:
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