Triple-Based Data Augmentation for Event Temporal Extraction via Reinforcement Learning from Human Feedback

Xiaobin Zhang, Liangjun Zang, Qianwen Liu, Shuchong Wei, Songlin Hu

Abstract: Event temporal relation (TempRel) extraction is a vital branch of information extraction. However, it is expensive to construct human-labeled training datasets, which leads to the lack of high-quality training data and inadequate training of the model. To mitigate this problem, we introduce a novel RLHF-based TempRel Data Augmentation method (RTDA) which aims at generating training data with high quality in terms of diversity and semantics fluency. Experimental results on three popular datasets TBD, TDD-Man, and TDD-Auto show that the model equipped with our method achieves state-of-the-art performance.

External IDs:dblp:conf/cscwd/ZhangZLWH24