Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network

Rui Li, Cheng Liu, Dazhi Jiang


Abstract
Recently, fine-tuning the pre-trained language model (PrLM) on labeled sentiment datasets demonstrates impressive performance. However, collecting labeled sentiment dataset is time-consuming, and fine-tuning the whole PrLM brings about much computation cost. To this end, we focus on multi-source unsupervised sentiment adaptation problem with the pre-trained features, which is more practical and challenging. We first design a dynamic feature network to fully exploit the extracted pre-trained features for efficient domain adaptation. Meanwhile, with the difference of the traditional source-target domain alignment methods, we propose a novel asymmetric mutual learning strategy, which can robustly estimate the pseudo-labels of the target domain with the knowledge from all the other source models. Experiments on multiple sentiment benchmarks show that our method outperforms the recent state-of-the-art approaches, and we also conduct extensive ablation studies to verify the effectiveness of each the proposed module.
Anthology ID:
2022.coling-1.604
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6934–6943
Language:
URL:
https://aclanthology.org/2022.coling-1.604
DOI:
Bibkey:
Cite (ACL):
Rui Li, Cheng Liu, and Dazhi Jiang. 2022. Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6934–6943, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network (Li et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.604.pdf