对比学习预训练和量子理论,用于跨语言的基于方面的情感分析
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
本研究介绍了CLQT,这是一种基于跨语言方面情绪分析 (ABSA) 的新框架. CLQT结合了对比学习和量子理论,以提高知识转移和低资源语言的分类准确性.
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:
- 基于跨语言方面的情绪分析 (ABSA) 具有挑战性,原因是高资源源语言和低资源目标语言之间的语言差距.
- 现有的方法通常依赖于多语言预训练语言模型 (mPLM) 和翻译,但忽视了语义相似性以外的因素,限制了性能.
- 跨语言有效的知识转移对于在不同语言背景下准确的ABSA至关重要.
研究的目的:
- 提出一个新的框架,CLQT,以提高跨语言ABSA的表现.
- 通过结合语义相似性以外的因素来解决现有方法的局限性.
- 提高知识转移和低资源目标语言的分类准确性.
主要方法:
- 开发了一种对比式学习策略,以协调源语言和目标语言之间的数据.
- 整合了一个量子网络,利用量子投影和纠来进行跨语言的知识传输.
- 结合了对比学习预训练与量子理论的统一框架 (CLQT).
主要成果:
- 在跨语言的ABSA任务中,CLQT框架取得了强的成果.
- 证明对目标语言的分类性能具有有益的整体影响.
- 实验结果验证了拟议的对比学习和量子网络集成的有效性.
结论:
- 该CLQT框架提供了一个有希望的方法来克服跨语言ABSA的挑战.
- 整合量子理论为有效的跨语言知识转移提供了新的机制.
- 该研究强调了将先进的机器学习技术与NLP任务的量子原理相结合的潜力.
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