Inproceedings,

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

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NAACL-HLT, (2018)

Abstract

We combine multi-task learning and semi- supervised learning by inducing a joint embed- ding space between disparate label spaces and learning transfer functions between label em- beddings, enabling us to jointly leverage un- labelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of se- quence classification tasks with disparate la- bel spaces. We outperform strong single and multi-task baselines and achieve a new state- of-the-art for topic-based sentiment analysis.

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