Learning Distributed Representations of Sentences from Unlabelled Data

Understand

Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data.

• This paper is a systematic comparison of models that learn such representations.
• We find that the optimal approach depends critically on the intended application.
• Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-linear models work best for building representation spaces that can be decoded with simple spatial distance metrics.