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Prior works on joint Information Extraction (IE) typically model instance(e.g., event triggers, entities, roles, relations) interactions byrepresentation enhancement, type dependencies scoring, or global decoding. Wefind that the previous models generally consider binary type dependency scoringof a pair of instances, and leverage local search such as beam search toapproximate global solutions. To better integrate cross-instance interactions,in this work, we introduce a joint IE framework (CRFIE) that formulates jointIE as a high-order Conditional Random Field. Specifically, we design binaryfactors and ternary factors to directly model interactions between not only apair of instances but also triplets. Then, these factors are utilized tojointly predict labels of all instances. To address the intractability problemof exact high-order inference, we incorporate a high-order neural decoder thatis unfolded from a mean-field variational inference method, which achievesconsistent learning and inference. The experimental results show that ourapproach achieves consistent improvements on three IE tasks compared with ourbaseline and prior work.

Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field