一种基于强化学习的生成方法,用于事件时间关系提取
Zhonghua Wu1,2, Wenzhong Yang1,2, Meng Zhang1,2
1School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China.
Entropy (Basel, Switzerland)
|March 28, 2025
概括
本研究引入了一种新的强化学习框架,用于事件时间关系提取,改善上下文词识别和生成精度. 该方法增强了自然语言处理模型,以更好地理解时间.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 事件时间关系提取对于理解文本至关重要.
- 现有的分类模型缺乏上下文字输出.
- 经过最大概率估计训练的生成模型面临着优化挑战.
研究的目的:
- 开发基于强化学习的生成框架,用于事件时间关系提取.
- 解决现有的分类和生成方法的局限性.
- 为了提高时间关系识别的准确性和上下文理解.
主要方法:
- 引入了基于强化学习的生成框架.
- 集成的依赖路径生成作为辅助任务.
- 利用了REINFORCE算法与一个新的奖励函数进行优化.
- 提出了一个基线政策梯度算法,以提高培训稳定性.
主要成果:
- 拟议的框架成功地输出了关键的上下文词语.
- 在 MATRES 和 TB-DENSE 数据集上取得了竞争性表现.
- 在时间预测和生成质量方面表现出更好的准确性.
结论:
- 强化学习生成框架为事件时间关系提取提供了一个有希望的解决方案.
- 整合依赖路径生成可以提高模型性能.
- 新的奖励函数和基准政策梯度算法提高了培训效率和稳定性.
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