Connecting the Dots: Event Graph Schema Induction with Path Language Modeling

By Manling Li, Qi Zeng, Ying Lin, Kyunghyun Cho, Heng Ji, Jonathan May, Nathanael Chambers, Clare R. Voss. 2020

91 citations recorded by the source corpus.

Event schemas can guide our understanding and ability to make predictions with respect to what might happen next. We propose a new Event Graph Schema, where two event types are connected through multiple paths involving entities that fill important roles in a coherent story. We then introduce Path Language Model, an auto-regressive language model trained on event-event paths, and select salient and coherent paths to probabilistically construct these graph schemas. We design two evaluation metrics, instance coverage and instance coherence, to evaluate the quality of graph schema induction, by checking when coherent event instances are covered by the schema graph. Intrinsic evaluations show that our approach is highly effective at inducing salient and coherent schemas. Extrinsic evaluations show the induced schema repository provides significant improvement to downstream end-to-end Information Extraction over a state-of-the-art joint neural extraction model, when used as additional global features to unfold instance graphs.

Subfields: Artificial Intelligence

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Citations in the corpus

ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2020, background): “…language understanding, and facilitates many real-world applications, such as tracking biomedical histories (Sun et al., 2013; Bethard et al., 2015, 2016, 2017), generating stories (Yao et al., 2019; Goldfarb-Tarrant et al., 2020), and forecasting social events (Li et al., 2020; Jin et al., 2020).”