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.

翻译：我们把多任务学习和半监督学习结合起来,在不同的标签空间和标签嵌入之间的学习转移功能之间形成一个联合嵌入空间,使我们能够共同利用无标签的数据和附带的附加说明数据集。我们用不同的标签空间评估我们关于各种序列分类任务的方法。我们的表现优于强大的单项和多任务基线,并实现了基于主题的新型情绪分析技术。