Unsupervised Learning of Sentence Representations Using Sequence Consistency

Computing universal distributed representations of sentences is a fundamental task in natural language processing. We propose a simple, yet surprisingly powerful unsupervised method to learn such representations by enforcing consistency constraints on sequences of tokens. We consider two classes of such constraints - sequences that form a sentence and between two sequences that form a sentence when merged. We learn a sentence encoder by training it to distinguish between consistent and inconsistent examples. Extensive evaluation on several transfer learning and linguistic probing tasks shows improved performance over strong unsupervised and supervised baselines, substantially surpassing them in several cases. In natural language processing, the use of distributed representations has become standard through the effective use of word embeddings.