Multi-Source Attention for Unsupervised Domain Adaptation

Xia Cui, Danushka Bollegala


Abstract
We model source-selection in multi-source Unsupervised Domain Adaptation (UDA) as an attention-learning problem, where we learn attention over the sources per given target instance. We first independently learn source-specific classification models, and a relatedness map between sources and target domains using pseudo-labelled target domain instances. Next, we learn domain-attention scores over the sources for aggregating the predictions of the source-specific models. Experimental results on two cross-domain sentiment classification datasets show that the proposed method reports consistently good performance across domains, and at times outperforming more complex prior proposals. Moreover, the computed domain-attention scores enable us to find explanations for the predictions made by the proposed method.
Anthology ID:
2020.aacl-main.87
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
Month:
December
Year:
2020
Address:
Suzhou, China
Editors:
Kam-Fai Wong, Kevin Knight, Hua Wu
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
873–883
Language:
URL:
https://aclanthology.org/2020.aacl-main.87
DOI:
Bibkey:
Cite (ACL):
Xia Cui and Danushka Bollegala. 2020. Multi-Source Attention for Unsupervised Domain Adaptation. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 873–883, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Multi-Source Attention for Unsupervised Domain Adaptation (Cui & Bollegala, AACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.aacl-main.87.pdf
Code
 summer1278/multi-source-attention