Extracting Temporal Event Relation with Syntax-guided Graph Transformer
This work addresses a key problem in natural language understanding for applications requiring temporal reasoning, representing an incremental improvement with novel method elements.
The paper tackles the challenge of extracting temporal relations between distant events in text by proposing a Syntax-guided Graph Transformer network, which significantly outperforms previous state-of-the-art methods on benchmark datasets like MATRES and TB-Dense.
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; This improvement also proves to be robust on the contrast set of MATRES. The code is publicly available at https://github.com/VT-NLP/Syntax-Guided-Graph-Transformer.