  In this work we propose a simple and efficient framework for learning
sentence representations from unlabelled data. Drawing inspiration from the
distributional hypothesis and recent work on learning sentence representations,
we reformulate the problem of predicting the context in which a sentence
appears as a classification problem. Given a sentence and its context, a
classifier distinguishes context sentences from other contrastive sentences
based on their vector representations. This allows us to efficiently learn
different types of encoding functions, and we show that the model learns
high-quality sentence representations. We demonstrate that our sentence
representations outperform state-of-the-art unsupervised and supervised
representation learning methods on several downstream NLP tasks that involve
understanding sentence semantics while achieving an order of magnitude speedup
in training time.
