Papers with TB-Dense
Temporal Cognitive Tree: A Hierarchical Modeling Approach for Event Temporal Relation Extraction (2024.findings-emnlp)
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| Challenge: | Recent studies focus on locating relative position of event pairs on timeline . hierarchical modeling approach neglects multidimensional information in temporal relation and hierarchy of reasoning. |
| Approach: | They propose a novel hierarchical modeling approach that mimics human logical reasoning by introducing a Temporal Cognitive Tree. |
| Outcome: | The proposed model outperforms existing methods on TB-Dense and MATRES datasets. |
RSGT: Relational Structure Guided Temporal Relation Extraction (2022.coling-1)
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| Challenge: | Temporal relation extraction (TRE) is crucial for natural language understanding. |
| Approach: | They propose a Temporal Relational Structure Guided Temporal Relations Extraction task to extract relational structure features that can fit for both inter-sentence and intra-sentent relations. |
| Outcome: | The proposed method improves on two well-known datasets, MATRES and TB-Dense, and can be used for clinical diagnosis and summarization. |
GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction (2025.findings-emnlp)
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| Challenge: | Existing approaches to analyzing large language models are limited by their pre-trained knowledge of Small Language Models(SLMs). |
| Approach: | They propose a global distance-aware modeling approach based on Large Language Models (LLMs) they first present a distance-based graph structure utilizing Graph Attention Network(GAT) |
| Outcome: | The proposed approach significantly improves minority relation class learning ability on two publicly available datasets, TB-Dense and MATRES. |
More than Classification: A Unified Framework for Event Temporal Relation Extraction (2023.acl-long)
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| Challenge: | Existing methods for event temporal relation extraction ignore meaning of relations and wipe out their intrinsic dependency. |
| Approach: | They propose a unified event temporal relation extraction framework that transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time points. |
| Outcome: | The proposed framework outperforms the state-of-the-art model on TB-Dense and MATRES by 0.3% on both datasets. |