Papers › TIMERS: Document-level Temporal Relation Extraction
TIMERS: Document-level Temporal Relation Extraction
Puneet Mathur, Rajiv Jain, Franck Dernoncourt, Vlad Morariu, Quan Hung Tran, Dinesh Manocha
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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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Temporal Relation Classification | MATRES | TIMERS | F1 | 82.3 | #4 of 4 | Archive leaderboard | report |
| Temporal Relation Classification | TB-Dense | TIMERS | F1 | 67.8 | #3 of 3 | Archive leaderboard | report |
| Temporal Relation Classification | TDDAuto | TIMERS | F1 | 71.1 | #3 of 3 | Archive leaderboard | report |
| Temporal Relation Classification | TDDMan | TIMERS | F1 | 45.5 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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