Contrastive Self-Supervised Learning for Commonsense Reasoning

Understand

We propose a self-supervised method to solve Pronoun Disambiguation and Winograd Schema Challenge problems.

• Our approach exploits the characteristic structure of training corpora related to so-called "trigger" words, which are responsible for flipping the answer in pronoun disambiguation.
• We achieve such commonsense reasoning by constructing pair-wise contrastive auxiliary predictions.
• To this end, we leverage a mutual exclusive loss regularized by a contrastive margin.