  Commonsense reasoning tasks such as commonsense knowledge graph completion
and commonsense question answering require powerful representation learning. In
this paper, we propose to learn commonsense knowledge representation by MICO, a
Multi-alternative contrastve learning framework on COmmonsense knowledge graphs
(MICO). MICO generates the commonsense knowledge representation by contextual
interaction between entity nodes and relations with multi-alternative
contrastive learning. In MICO, the head and tail entities in an $(h,r,t)$
knowledge triple are converted to two relation-aware sequence pairs (a premise
and an alternative) in the form of natural language. Semantic representations
generated by MICO can benefit the following two tasks by simply comparing the
distance score between the representations: 1) zero-shot commonsense question
answering task; 2) inductive commonsense knowledge graph completion task.
Extensive experiments show the effectiveness of our method.

Computation and Language