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://preview.aclanthology.org/build-pipeline-with-new-library/2020.aacl-main.87/
- DOI:
- 10.18653/v1/2020.aacl-main.87
- 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)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.aacl-main.87.pdf
- Code
- summer1278/multi-source-attention