mlsanityData to 7 Oct 2026

Extracting Temporal Event Relation with Syntax-guided Graph Transformer

arXiv 2104.09570 ↗First posted 19 Apr 2021cs.CL cs.AIcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 24 of its 54 references that are in the map. First posted on arXiv 19 Apr 2021.

Builds on

24 of 54 references.

References · 24

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12 more
  1. 2020Joint Constrained Learning for Event-Event Relation Extractioncited by 53
  2. 2019Joint Event and Temporal Relation Extraction with Shared Representations and Structured Predictioncited by 52
  3. 2021The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Predictioncited by 39
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  5. 2019An Improved Neural Baseline for Temporal Relation Extractioncited by 32
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  7. 2019Deep Structured Neural Network for Event Temporal Relation Extractioncited by 24
  8. 2019Path-Augmented Graph Transformer Networkcited by 22
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  10. 2020Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inferencecited by 20
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Citing papers per year: 2023: 4; 2024: 3; 2025: 7; 2026: 2.

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  1. 2023Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoningcited by 12
  2. 2023Once Upon a textitTime in textitGraph: Relative-Time Pretraining for Complex Temporal Reasoningcited by 10
  3. 2024Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?cited by 6
  4. 2024Unveiling Divergent Inductive Biases of LLMs on Temporal Datacited by 4
  5. 2025Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approachcited by 4
  6. 2023Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answeringcited by 3
  7. 2025HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relationscited by 3
  8. 2025GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extractioncited by 2
  9. 2024TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systemscited by 1
  10. 2025Transformer-Based Temporal Information Extraction and Application: A Reviewcited by 1
  11. 2023RE2: Region-Aware Relation Extraction from Visually Rich Documentscited by 0
  12. 2025Exploring Graph Representations of Logical Forms for Language Modelingcited by 0
4 more
  1. 2025Chaining Event Spans for Temporal Relation Groundingcited by 0
  2. 2025Building Patient Journeys in Hebrew: A Language Model for Clinical Timeline Extractioncited by 0
  3. 2026WISTERIA: Weak Implicit Signal-based Temporal Relation Extraction with Attentioncited by 0
  4. 2026Looking for the Bottleneck in Fine-grained Temporal Relation Classificationcited by 0