Logic Induced High-Order Reasoning Network for Event-Event Relation Extraction

TL;DR

This paper introduces LogicERE, a novel high-order reasoning network that models logical constraints directly within event graphs for improved event-event relation extraction, outperforming existing methods.

Contribution

The work proposes a logic constraint induced graph and a relational graph transformer to perform high-order reasoning without external tools, ensuring logical coherence in event relation extraction.

Findings

Achieves state-of-the-art results on benchmark datasets.

Effectively models logical constraints to improve relation extraction.

Outperforms previous methods in accuracy and coherence.

Abstract

To understand a document with multiple events, event-event relation extraction (ERE) emerges as a crucial task, aiming to discern how natural events temporally or structurally associate with each other. To achieve this goal, our work addresses the problems of temporal event relation extraction (TRE) and subevent relation extraction (SRE). The latest methods for such problems have commonly built document-level event graphs for global reasoning across sentences. However, the edges between events are usually derived from external tools heuristically, which are not always reliable and may introduce noise. Moreover, they are not capable of preserving logical constraints among event relations, e.g., coreference constraint, symmetry constraint and conjunction constraint. These constraints guarantee coherence between different relation types,enabling the generation of a uniffed event evolution graph. In this work, we propose a novel method named LogicERE, which performs high-order event relation reasoning through modeling logic constraints. Speciffcally, different from conventional event graphs, we design a logic constraint induced graph (LCG) without any external tools. LCG involves event nodes where the interactions among them can model the coreference constraint, and event pairs nodes where the interactions among them can retain the symmetry constraint and conjunction constraint. Then we perform high-order reasoning on LCG with relational graph transformer to obtain enhanced event and event pair embeddings. Finally, we further incorporate logic constraint information via a joint logic learning module. Extensive experiments demonstrate the effectiveness of the proposed method with state-of-the-art performance on benchmark datasets.