Shuaicheng Zhang, Lifu Huang


Extracting temporal relations (e.g., before, after, and simultaneous) among events is crucial to natural language understanding. One of the key challenges of this problem is that when the events of interest are far away in text, the context in-between often becomes complicated, making it challenging to resolve the temporal relationship between them. This paper thus proposes a new Syntax-guided Graph Transformer network (SGT) to mitigate this issue, by (1) explicitly exploiting the connection between two events based on their dependency parsing trees, and (2) automatically locating temporal cues between two events via a novel syntax-guided attention mechanism. Experiments on two benchmark datasets, MATRES and TB-DENSE, show that our approach significantly outperforms previous state-of-the-art methods on both end-to-end temporal relation extraction and temporal relation classification with up to 7.9% absolute F-score gain; This improvement also proves to be robust on the contrast set of MATRES. We will make all the programs publicly available once the paper is accepted.

Shuaicheng Zhang, Qiang Ning, Lifu HuangExtracting Temporal Event Relation with Syntax-guided Graph Transformer. NAACL-HLT (Findings) 2022: 379-390


Shuaicheng Zhang

Lifu Huang

Publication Details

Date of publication:
July 10, 2022
Association for Computational Linguistics
Page number(s):
Findings of the Association for Computational Linguistics: NAACL 2022