Papers with temporal relation extraction
EventPlus: A Temporal Event Understanding Pipeline (2021.naacl-demos)
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Mingyu Derek Ma, Jiao Sun, Mu Yang, Kung-Hsiang Huang, Nuan Wen, Shikhar Singh, Rujun Han, Nanyun Peng
| Challenge: | Event information is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. |
| Approach: | They propose a temporal event understanding pipeline that integrates state-of-the-art components. |
| Outcome: | The proposed pipeline can be easily adapted to other domains, including biomedical domains. |
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction (D19-1)
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| Challenge: | Existing systems treat this task as a pipeline of two separate subtasks, i.e., event extraction and temporal relation classification. |
| Approach: | They propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. |
| Outcome: | The proposed method improves both event extraction and temporal relation extraction over state-of-the-art systems. |
Joint Constrained Learning for Event-Event Relation Extraction (2020.emnlp-main)
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| Challenge: | Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. |
| Approach: | They propose a joint constrained learning framework that enforces logical constraints within and across multiple temporal and subevent relations of events by converting constraints into differentiable learning objectives. |
| Outcome: | The proposed framework outperforms SOTA methods on benchmarks for temporal relation extraction and event hierarchy construction. |
ConTempo: A Unified Temporally Contrastive Framework for Temporal Relation Extraction (2024.findings-acl)
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| Challenge: | Temporal relation extraction (TRE) is a task of classifying temporal relations between events conveyed in narratives. |
| Approach: | They propose a Temporally Contrastive learning model that increases the model’s awareness of the meaning of temporal relations by leveraging their symmetric or antisymmetric properties. |
| Outcome: | The proposed model improves the model's representation of meaning of temporal relations and its ability to integrate with the underlying temporal calculus. |
Event Semantic Classification in Context (2024.findings-eacl)
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| Challenge: | In this work, we focus on the semantic classification of events in context to help machines gain a deeper understanding of events. |
| Approach: | They propose to integrate event semantics into downstream tasks to help machines understand events better. |
| Outcome: | The proposed model improves the understanding of events in context. |
DCT-Centered Temporal Relation Extraction (2022.coling-1)
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| Challenge: | Existing work on temporal relation extraction focuses on extracting temporal relations between events . previous work on relation extraction focused on focusing on event-centered tasks . |
| Approach: | They propose a temporal relation extraction model that unifies events, timexes and DCT . they propose combining event mentions, time expressions and document creation time into a sentence-style model . |
| Outcome: | The proposed model outperforms baselines on E-E, E-T and E-D significantly. |
BeDiscovER: The Benchmark of Discourse Understanding in the Era of Reasoning Language Models (2026.eacl-long)
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| Challenge: | BeDiscovER evaluates the discourse-level knowledge of modern LLMs . state-of-the-art models exhibit strong performance in arithmetic aspect of temporal reasoning, but struggle with long-dependency reasoning and some subtle semantic and discourse phenomena, such as rhetorical relation classification. |
| Approach: | They evaluate open-source LLMs Qwen3 series, DeepSeek-R1, and frontier reasoning model GPT-5-mini on BeDiscovER . they find that models exhibit strong performance in arithmetic aspect of temporal reasoning, but struggle with long-dependency reasoning and some subtle semantic and discourse phenomena . |
| Outcome: | The proposed framework evaluates open-source LLMs Qwen3 series, DeepSeek-R1, and frontier reasoning model GPT-5-mini. |
Word-Level Loss Extensions for Neural Temporal Relation Classification (C18-1)
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| Challenge: | Unsupervised pre-trained word embeddings are used for many tasks in natural language processing to leverage unlabeled textual data. |
| Approach: | They extend the model's task loss with an unsupervised auxiliary loss on the word-embedding level of the model to ensure that the learned word representations contain both task-specific features and more general features. |
| Outcome: | The proposed model improves on the task of extracting narrative containment relations from clinical records using a general-domain part-of-speech tagger as linguistic resource. |
Distinguishing Between Foreground and Background Events in News (2020.coling-main)
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| Challenge: | a new task is needed to distinguish between foreground and background events in news articles . |
| Approach: | They propose a task of distinguishing between foreground and background events in news articles . they also identify the general temporal position of background events relative to the foregoing period . |
| Outcome: | The proposed model achieves good performance on a dataset of news articles . |
Consistent Discourse-level Temporal Relation Extraction Using Large Language Models (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models (LLMs) have spurred research on temporal relation extraction tasks. |
| Approach: | They propose a framework to improve LLMs’ temporal relation extraction capabilities using context selection, prompts inspired by Allen’s interval algebra and reflection-based consistency learning. |
| Outcome: | The proposed framework improves LLMs’ extraction capabilities by focusing on context selection, prompts inspired by Allen’s interval algebra and reflection-based consistency learning. |