对使用多任务和知识基础的中文文本进行情感解释性分析
Xinyue Quan1, Xiang Xie1, Yang Liu1
1Beijing Institute of Technology, Beijijng, China.
Frontiers in artificial intelligence
|January 22, 2024
概括
这项研究通过注意力分析和外部知识提高了情绪模型的解释性. 这种新的方法提高了理解和证据提取,提高了模型的性能和可靠性在关键应用中.
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
- 机器学习 机器学习
背景情况:
- 深度学习模型虽然强大,但经常充当"黑子",由于解释能力低,限制了它们在关键应用中的使用.
- 解释情绪分析模型对于理解他们的决策过程和确保可靠的结果至关重要.
研究的目的:
- 开发一种全面的方法来提高情绪分析模型的可解释性.
- 解决情绪模型中解释性低,证据提取不完整的挑战,特别是对于中国文本.
主要方法:
- 通过训练情绪分类和生成任务来实现基于注意力的分析,以捕捉多视角的注意力得分.
- 集成了一个外部知识库,并利用角色分数来改进和完整的情感证据短语提取.
- 在情绪解释性数据集上评估了该方法.
主要成果:
- 在模型性能方面取得了显著的改进.
- 实现了1.3%的精度提升,13%的宏观F1得分提升,23%的平均精度 (MAP) 提升.
结论:
- 拟议的方法通过结合注意力机制和外部知识,有效地提高了情绪模型的解释性.
- 该方法为更透明和可靠的情绪分析提供了强大的解决方案,特别有利于中文语言处理.
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