Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning
Script reasoning infers subsequent events from a given event chain, which involves the ability to understand relations between events. A human-labeled script reasoning dataset is usually of small size with limited event relations, which highlights the necessity to leverage external eventuality knowledge graphs (KG) consisting of numerous triple facts to describe the inferential relation between events. Existing methods adopt a retrieval and integration paradigm to focus merely on the graph triples that have event overlap with a script, but ignore much more supportive triples in the KG with similar inferential patterns, leading to under-exploiting. To fully exploit the KG, we propose a knowledge model to learn the inferential relations between events from the whole eventuality KG and then support downstream models by directly capturing the relation between events in a script. We further present a neural script adapter to extend the knowledge model for inferring the associated relations between an event chain and a subsequent event candidate. We evaluate the proposed approach on a popular multi-choice narrative cloze task for script reasoning and achieve new state-ofthe-art accuracy, compared with baselines either incorporating external KG or not.
