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Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
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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 sequence classification tasks with disparate label 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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Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing
Across nine NLP datasets, multi-task learning with linguistic-hierarchy supervision gives the largest average gain among five MTL mechanisms, and the best hybrid combines hierarchies, gating, and label embedding, not ...
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