一个因果融合框架与信息瓶,用于对话的因果情绪卷入
Xinxin Su1, Zhen Huang1, Menglong Lu2
1National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, No. 109, Deya Road, Kaifu District, Changsha, China.
概括
这项研究引入了对话性因果情感参与 (CEE) 的新框架,该框架考虑了情感原因之间的关系. 拟议的模型显著改善了在对话中识别情绪触发器的情况.
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:
- 谈话因果情绪参与 (CEE) 旨在识别对话中的情绪触发因素.
- 现有的CEE模型往往忽视了关键的因果关系和相关因果相关特征.
- 当前的方法也可能会受到不相关的情感信息的影响.
研究的目的:
- 提出一个新的框架,原因融合框架与信息瓶 (CFIB),以解决CEE的局限性.
- 为了有效地模拟对话情感内因果关系之间的关系.
- 为了加强对因果相关特征的关注,并减轻不相关的情绪信息.
主要方法:
- 开发了因果融合与信息瓶 (CFIB) 的框架.
- 纳入机制以捕捉对话中的因果关系.
- 强调与原因相关的特征,同时过不相关的情感数据.
主要成果:
- 在两个基准数据集上,CFIB取得了最先进的业绩.
- 该框架在CEE任务中表现出卓越的有效性.
- 实验结果验证了模型处理复杂因果关系的能力.
结论:
- 拟议的CFIB框架为对话性因果情感参与提供了显著的进步.
- 考虑因果关系和精炼特征选择对中东欧地区至关重要.
- CFIB提供了一种强大而有效的解决方案,用于识别对话中的情绪触发器.
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