Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation

Understand

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA).

• To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR.
• Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines.
• The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors.