Abstract
Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive questions. This necessitates not only the parsing of temporal information within questions but also the identification and understanding of time-evolving facts to generate accurate answers. However, current large language models still have limited sensitivity to temporal information and their inadequate temporal reasoning capabilities. In this paper, we propose a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning. Experimental results on four TSQA datasets demonstrate that our framework significantly outperforms existing LLMs in TSQA tasks, marking a step forward in bridging the performance gap between machine and human temporal understanding and reasoning.- Anthology ID:
- 2024.findings-emnlp.848
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 14495–14508
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.848/
- DOI:
- 10.18653/v1/2024.findings-emnlp.848
- Cite (ACL):
- Wanqi Yang, Yanda Li, Meng Fang, and Ling Chen. 2024. Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 14495–14508, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering (Yang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.848.pdf