# An Improved Neural Baseline for Temporal Relation Extraction

arXiv: https://arxiv.org/abs/1909.00429 · HTML: https://mlsanity.com/paper/1909.00429/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2019.

## Built on by (most cited first, 30 of 32)

- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- [Test of Time: Instilling Video-Language Models with a Sense of Time](https://mlsanity.com/paper/2301.02074/) (2023)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [EventPlus: A Temporal Event Understanding Pipeline](https://mlsanity.com/paper/2101.04922/) (2021)
- [Extracting Temporal Event Relation with Syntax-guided Graph Transformer](https://mlsanity.com/paper/2104.09570/) (2021)
- [ROCK: Causal Inference Principles for Reasoning about Commonsense Causality](https://mlsanity.com/paper/2202.00436/) (2022)
- [More than Classification: A Unified Framework for Event Temporal Relation Extraction](https://mlsanity.com/paper/2305.17607/) (2023)
- [Extracting Event Temporal Relations via Hyperbolic Geometry](https://mlsanity.com/paper/2109.05527/) (2021)
- [Event Temporal Relation Extraction with Bayesian Translational Model](https://mlsanity.com/paper/2302.04985/) (2023)
- [Zero-Shot On-the-Fly Event Schema Induction](https://mlsanity.com/paper/2210.06254/) (2022)
- [TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles](https://mlsanity.com/paper/2310.17802/) (2023)
- [Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation](https://mlsanity.com/paper/2404.01532/) (2024)
- [Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction](https://mlsanity.com/paper/2209.00568/) (2022)
- [GKG-LLM: A Unified Framework for Generalized Knowledge Graph Construction](https://mlsanity.com/paper/2503.11227/) (2025)
- [A Solver-Free Framework for Scalable Learning in Neural ILP Architectures](https://mlsanity.com/paper/2210.09082/) (2022)
- [tieval: An Evaluation Framework for Temporal Information Extraction Systems](https://mlsanity.com/paper/2301.04643/) (2023)
- [Unveiling Divergent Inductive Biases of LLMs on Temporal Data](https://mlsanity.com/paper/2404.01453/) (2024)
- [Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering](https://mlsanity.com/paper/2310.19292/) (2023)
- [Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs](https://mlsanity.com/paper/2403.15273/) (2024)
- [Cascading Large Language Models for Salient Event Graph Generation](https://mlsanity.com/paper/2406.18449/) (2024)
- [Beyond Pairwise: Global Zero-shot Temporal Graph Generation](https://mlsanity.com/paper/2502.11114/) (2025)
- [All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison](https://mlsanity.com/paper/2310.18827/) (2023)
- [Formulation Comparison for Timeline Construction using LLMs](https://mlsanity.com/paper/2403.00990/) (2024)
- [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)
- [Context-Dependent Semantic Parsing for Temporal Relation Extraction](https://mlsanity.com/paper/2112.00894/) (2021)
- [Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Model](https://mlsanity.com/paper/2201.06125/) (2022)
- [Building Patient Journeys in Hebrew: A Language Model for Clinical Timeline Extraction](https://mlsanity.com/paper/2512.11502/) (2025)

## 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.