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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