Script Event Prediction via Multilingual Event Graph Networks

Abstract

Predicting what happens next in text plays a critical role in building NLP applications. Many methods including count-based and neural-network-based have been proposed to tackle the task called script event prediction: predicting the most suitable subsequent event from a candidate list given a chain of narrative events (context). However, two problems including event ambiguity and evidence bias hinder the performance of these monolingual approaches. The former means that some events in the event chain are ambiguous. The latter means that both the wrong and correct candidate events can obtain sufficient support from the event context. In this article, we propose a novel multilingual approach to address two issues simultaneously. Specifically, to alleviate the event ambiguity problem, we project the monolingual event chains to parallel cross-lingual event chains, which can provide complementary information for monolingual event disambiguation. To deal with the evidence bias problem, we construct two monolingual event graphs and a cross-lingual event aligned graph to fully explore connections between events. What’s more, we design a graph attention mechanism to model the confidence of the complement clues, which controls the information integration from various languages. By modeling the events with graphs instead of pairs or chains, the model can compare the candidate subsequent events simultaneously and choose the more suitable subsequent event as the final answer. Extensive experiments were conducted on the widely used New York Times corpus for script event prediction task and experimental results show that our approach outperforms previous models.