Staff – University of Copenhagen

Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces

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Documents

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.
Original languageEnglish
Title of host publicationProceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : (Long Papers)
Number of pages11
Volume1
PublisherAssociation for Computational Linguistics
Publication date2018
Pages1896–1906
DOIs
Publication statusPublished - 2018
Event16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - New Orleans, United States
Duration: 1 Jun 20186 Jun 2018

Conference

Conference16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
LandUnited States
ByNew Orleans
Periode01/06/201806/06/2018

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ID: 195047317