Abstract
We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state of the art for aspect-based and topic-based sentiment analysis.- Anthology ID:
- N18-1172
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1896–1906
- Language:
- URL:
- https://aclanthology.org/N18-1172
- DOI:
- 10.18653/v1/N18-1172
- Cite (ACL):
- Isabelle Augenstein, Sebastian Ruder, and Anders Søgaard. 2018. Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1896–1906, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces (Augenstein et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/N18-1172.pdf
- Code
- coastalcph/mtl-disparate