Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces

Isabelle Augenstein, Sebastian Ruder, Anders Søgaard


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://www.aclweb.org/anthology/N18-1172
DOI:
10.18653/v1/N18-1172
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1172.pdf
Video:
 http://vimeo.com/277671362