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PIPER: A logic-driven deep contrastive optimization pipeline for event temporal reasoning.
1School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
This study introduces PIPER, a novel pipeline for event temporal reasoning. PIPER enhances joint optimization of neural networks and temporal logic rules for more interpretable and flexible event relation extraction.
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Area of Science:
- Natural Language Processing
- Artificial Intelligence
- Computational Linguistics
Background:
- Event temporal relation extraction is crucial for information extraction.
- Existing methods often involve independent feature engineering and post-processing, leading to optimization inconsistencies.
- Current joint optimization methods for neural networks and temporal logic rules lack interpretability and flexibility.
Purpose of the Study:
- To propose PIPER, a logic-driven deep contrastive optimization pipeline for event temporal reasoning.
- To address limitations in interpretability, flexibility, and feature-rule interaction in existing models.
- To improve the performance of event temporal relation extraction.
Main Methods:
- PIPER employs joint optimization (multi-stage and single-stage) with independent rule losses for enhanced interpretability and flexibility.
- A hierarchical graph distillation network is introduced to capture richer syntactic information.
- This network facilitates effective interaction between low-level features and high-level rules during training.
Main Results:
- The proposed PIPER model demonstrates competitive performance on the TB-Dense and MATRES datasets.
- PIPER achieves improved event temporal reasoning compared to recent advances.
- The model exhibits enhanced interpretability and flexibility in handling temporal logic rules.
Conclusions:
- PIPER offers a more interpretable and flexible approach to event temporal relation extraction.
- The integration of syntactic information via graph distillation improves model performance.
- The proposed pipeline represents a significant advancement in logic-driven deep learning for temporal reasoning.