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An Improved Neural Baseline for Temporal Relation Extraction

arXiv 1909.00429 ↗First posted 1 Sep 2019cs.CLcited by 32

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Abstract (arXiv)

Determining temporal relations (e.g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amounts of high-quality training data. Consequently, neural approaches have not been widely used on it, or showed only moderate improvements. This paper proposes a new neural system that achieves about 10% absolute improvement in accuracy over the previous best system (25% error reduction) on two benchmark datasets. The proposed system is trained on the state-of-the-art MATRES dataset and applies contextualized word embeddings, a Siamese encoder of a temporal common sense knowledge base, and global inference via integer linear programming (ILP). We suggest that the new approach could serve as a strong baseline for future research in this area.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2019.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 7; 2024: 6; 2025: 5; 2026: 2.

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  1. 2022MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extractioncited by 65
  2. 2023Test of Time: Instilling Video-Language Models with a Sense of Timecited by 51
  3. 2021The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Predictioncited by 39
  4. 2021What is Event Knowledge Graph: A Surveycited by 19
  5. 2021EventPlus: A Temporal Event Understanding Pipelinecited by 17
  6. 2021Extracting Temporal Event Relation with Syntax-guided Graph Transformercited by 16
  7. 2022ROCK: Causal Inference Principles for Reasoning about Commonsense Causalitycited by 16
  8. 2023More than Classification: A Unified Framework for Event Temporal Relation Extractioncited by 14
  9. 2021Extracting Event Temporal Relations via Hyperbolic Geometrycited by 13
  10. 2023Event Temporal Relation Extraction with Bayesian Translational Modelcited by 10
  11. 2022Zero-Shot On-the-Fly Event Schema Inductioncited by 9
  12. 2024Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generationcited by 8
20 more
  1. 2023TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articlescited by 7
  2. 2022Distilling Multi-Scale Knowledge for Event Temporal Relation Extractioncited by 6
  3. 2025GKG-LLM: A Unified Framework for Generalized Knowledge Graph Constructioncited by 6
  4. 2022A Solver-Free Framework for Scalable Learning in Neural ILP Architecturescited by 5
  5. 2023tieval: An Evaluation Framework for Temporal Information Extraction Systemscited by 5
  6. 2024Unveiling Divergent Inductive Biases of LLMs on Temporal Datacited by 4
  7. 2023Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answeringcited by 3
  8. 2024Event Temporal Relation Extraction based on Retrieval-Augmented on LLMscited by 3
  9. 2024Cascading Large Language Models for Salient Event Graph Generationcited by 3
  10. 2023All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparisoncited by 2
  11. 2024Formulation Comparison for Timeline Construction using LLMscited by 2
  12. 2025Beyond Pairwise: Global Zero-shot Temporal Graph Generationcited by 2
  13. 2025GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extractioncited by 2
  14. 2024TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systemscited by 1
  15. 2025Transformer-Based Temporal Information Extraction and Application: A Reviewcited by 1
  16. 2021Context-Dependent Semantic Parsing for Temporal Relation Extractioncited by 0
  17. 2022Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Modelcited by 0
  18. 2025Building Patient Journeys in Hebrew: A Language Model for Clinical Timeline Extractioncited by 0
  19. 2026WISTERIA: Weak Implicit Signal-based Temporal Relation Extraction with Attentioncited by 0
  20. 2026Looking for the Bottleneck in Fine-grained Temporal Relation Classificationcited by 0