Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning

Understand

This work presents a novel objective function for the unsupervised training of neural network sentence encoders.

• It exploits signals from paragraph-level discourse coherence to train these models to understand text.
• Our objective is purely discriminative, allowing us to train models many times faster than was possible under prior methods, and it yields models which perform well in extrinsic evaluations.