# Extracting Temporal Event Relation with Syntax-guided Graph Transformer

arXiv: https://arxiv.org/abs/2104.09570 · HTML: https://mlsanity.com/paper/2104.09570/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 54 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://mlsanity.com/paper/1909.00277/) (2019)
- [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://mlsanity.com/paper/1708.03743/) (2017)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](https://mlsanity.com/paper/2005.00242/) (2020)
- [Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction](https://mlsanity.com/paper/1909.05360/) (2019)
- [Joint Constrained Learning for Event-Event Relation Extraction](https://mlsanity.com/paper/2010.06727/) (2020)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning](https://mlsanity.com/paper/2012.15283/) (2020)
- [An Improved Neural Baseline for Temporal Relation Extraction](https://mlsanity.com/paper/1909.00429/) (2019)
- [A Structured Learning Approach to Temporal Relation Extraction](https://mlsanity.com/paper/1906.04943/) (2019)
- [Deep Structured Neural Network for Event Temporal Relation Extraction](https://mlsanity.com/paper/1909.10094/) (2019)
- [Path-Augmented Graph Transformer Network](https://mlsanity.com/paper/1905.12712/) (2019)
- [Fine-Grained Temporal Relation Extraction](https://mlsanity.com/paper/1902.01390/) (2019)
- [Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference](https://mlsanity.com/paper/2012.08790/) (2020)
- [Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture](https://mlsanity.com/paper/1703.05851/) (2017)
- [Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures](https://mlsanity.com/paper/1707.01075/) (2017)

## Built on by (most cited first, 16 of 16)

- [Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning](https://mlsanity.com/paper/2305.14970/) (2023)
- [Once Upon a textitTime in textitGraph: Relative-Time Pretraining for Complex Temporal Reasoning](https://mlsanity.com/paper/2310.14709/) (2023)
- [Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?](https://mlsanity.com/paper/2410.10476/) (2024)
- [Unveiling Divergent Inductive Biases of LLMs on Temporal Data](https://mlsanity.com/paper/2404.01453/) (2024)
- [Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach](https://mlsanity.com/paper/2503.18085/) (2025)
- [Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering](https://mlsanity.com/paper/2310.19292/) (2023)
- [HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations](https://mlsanity.com/paper/2510.10864/) (2025)
- [GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction](https://mlsanity.com/paper/2508.20828/) (2025)
- [TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems](https://mlsanity.com/paper/2406.14825/) (2024)
- [Transformer-Based Temporal Information Extraction and Application: A Review](https://mlsanity.com/paper/2504.07470/) (2025)
- [RE2: Region-Aware Relation Extraction from Visually Rich Documents](https://mlsanity.com/paper/2305.14590/) (2023)
- [Exploring Graph Representations of Logical Forms for Language Modeling](https://mlsanity.com/paper/2505.14523/) (2025)
- [Chaining Event Spans for Temporal Relation Grounding](https://mlsanity.com/paper/2506.14213/) (2025)
- [Building Patient Journeys in Hebrew: A Language Model for Clinical Timeline Extraction](https://mlsanity.com/paper/2512.11502/) (2025)
- [WISTERIA: Weak Implicit Signal-based Temporal Relation Extraction with Attention](https://mlsanity.com/paper/2603.23319/) (2026)
- [Looking for the Bottleneck in Fine-grained Temporal Relation Classification](https://mlsanity.com/paper/2604.24620/) (2026)

## 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.