基于高维多尺度信息的核心参考分辨率
Yu Wang1,2, Zenghui Ding1, Tao Wang1
1Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
Entropy (Basel, Switzerland)
|June 26, 2024
概括
这项研究通过改进长文件的BERT文本编码来增强自然语言处理的核心引用分辨率. 一个新的模块提高了性能,使模型更好地理解文本跨越扩展的文本跨度.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 核心引用解析是自然语言处理 (NLP) 的基本任务,对于文本理解至关重要.
- 对传统的文本级编码方法来说,评估长文本的相似性会带来挑战.
- 现有的模型很难在扩展的文档中有效地捕捉全球背景.
研究的目的:
- 调查方法,以加强全球信息收集在BERT编码NLP任务.
- 设计一个新的模块,以提高BERT在各种文本跨度中的适用性.
- 为了应对在核心引用解析中评估长文本相似性的挑战.
主要方法:
- 对改善BERT全球信息收集的方法进行比较分析.
- 开发一个针对不同文本范围量身定制的多尺度上下文信息模块.
- 用维度扩展来增强线性可分性的应用.
- 使用交叉损失作为优化目标.
主要成果:
- 拟议的多尺度上下文信息模块与BERT和跨度BERT集成.
- 在F1分数中,BERT编码性能有0.5%的改善.
- 在F1分数中,BERT编码性能提高了0.2%.
- 该模块展示了处理长文本背景的增强能力.
结论:
- 开发的多规模上下文信息模块有效地提高了BERT在核心引用解决任务上的表现,特别是在长文本方面.
- 这种方法提高了模型捕获全球信息和处理不同文本跨度的能力.
- 未来的工作可以探索该模块在其他NLP任务中的进一步优化和应用.
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