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A lot of the recent success in natural language processing (NLP) has beendriven by distributed vector representations of words trained on large amountsof text in an unsupervised manner. These representations are typically used asgeneral purpose features for words across a range of NLP problems. However,extending this success to learning representations of sequences of words, suchas sentences, remains an open problem. Recent work has explored unsupervised aswell as supervised learning techniques with different training objectives tolearn general purpose fixed-length sentence representations. In this work, wepresent a simple, effective multi-task learning framework for sentencerepresentations that combines the inductive biases of diverse trainingobjectives in a single model. We train this model on several data sources withmultiple training objectives on over 100 million sentences. Extensiveexperiments demonstrate that sharing a single recurrent sentence encoder acrossweakly related tasks leads to consistent improvements over previous methods. Wepresent substantial improvements in the context of transfer learning andlow-resource settings using our learned general-purpose representations.

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning