TIMERS: Document-level Temporal Relation Extraction

We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through a Gated Relational-GCN. Extensive experiments show that the proposed model outperforms previous methods by 5-18{\%} on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to our discourse-level modeling.

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Temporal Relation Classification MATRES TIMERS F1 82.3 # 4
Temporal Relation Classification TB-Dense TIMERS F1 67.8 # 3
Temporal Relation Classification TDDAuto TIMERS F1 71.1 # 3
Temporal Relation Classification TDDMan TIMERS F1 45.5 # 3

Methods


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