# Looking for the Bottleneck in Fine-grained Temporal Relation Classification

arXiv: https://arxiv.org/abs/2604.24620 · HTML: https://mlsanity.com/paper/2604.24620/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Builds on (most cited first, 26 of 26)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model](https://mlsanity.com/paper/2502.02737/) (2025)
- [A System for Massively Parallel Hyperparameter Tuning](https://mlsanity.com/paper/1810.05934/) (2018)
- ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://mlsanity.com/paper/1909.03065/) (2019)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Temporal Reasoning on Implicit Events from Distant Supervision](https://mlsanity.com/paper/2010.12753/) (2020)
- [Joint Constrained Learning for Event-Event Relation Extraction](https://mlsanity.com/paper/2010.06727/) (2020)
- [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)
- [Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource](https://mlsanity.com/paper/1804.06020/) (2018)
- [Extracting Temporal Event Relation with Syntax-guided Graph Transformer](https://mlsanity.com/paper/2104.09570/) (2021)
- [Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction](https://mlsanity.com/paper/2210.04992/) (2022)
- [More than Classification: A Unified Framework for Event Temporal Relation Extraction](https://mlsanity.com/paper/2305.17607/) (2023)
- [Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events](https://mlsanity.com/paper/2004.04295/) (2020)
- [Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture](https://mlsanity.com/paper/1703.05851/) (2017)
- [A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract)](https://mlsanity.com/paper/2005.06527/) (2020)
- [Effective Distant Supervision for Temporal Relation Extraction](https://mlsanity.com/paper/2010.12755/) (2020)
- [Cross-lingual Cross-temporal Summarization: Dataset, Models, Evaluation](https://mlsanity.com/paper/2306.12916/) (2023)
- [TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles](https://mlsanity.com/paper/2310.17802/) (2023)
- [Exploring Contextualized Neural Language Models for Temporal Dependency Parsing](https://mlsanity.com/paper/2004.14577/) (2020)
- [tieval: An Evaluation Framework for Temporal Information Extraction Systems](https://mlsanity.com/paper/2301.04643/) (2023)
- [Formulation Comparison for Timeline Construction using LLMs](https://mlsanity.com/paper/2403.00990/) (2024)

## Abstract (arXiv)

Temporal relation classification is the task of determining the temporal relation between pairs of temporal entities in a text. Despite recent advancements in natural language processing, temporal relation classification remains a considerable challenge. Early attempts framed this task using a comprehensive set of temporal relations between events and temporal expressions. However, due to the task complexity, datasets have been progressively simplified, leading recent approaches to focus on the relations between event pairs and to use only a subset of relations. In this work, we revisit the broader goal of classifying interval relations between temporal entities by considering the full set of relations that can hold between two time intervals. The proposed approach, Interval from Point, involves first classifying the point relations between the endpoints of the temporal entities and then decoding these point relations into an interval relation. Evaluation on the TempEval-3 dataset shows that this approach can yield effective results, achieving a temporal awareness score of 70.1 percent, a new state-of-the-art on this benchmark.