跨语言NLU的交互框架,用于车载对话
Xinlu Li1, Liangkuan Fang1, Lexuan Zhang1
1School of Artificial Intelligence and Big Data, Hefei University, Hefei 230061, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了一种基于注意力的交互式对比学习框架 (IABCL),以改善车辆的跨语言自然语言理解 (NLU). 新方法增强了车载对话系统,用于不同语言的使用者,显示了显著的准确性改进.
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
- 人与计算机的交互
背景情况:
- 全球化增加了车载通信中的语言多样性.
- 现有系统在车辆中的跨语言自然语言理解 (NLU) 中扎.
- 需要加强NLU以支持汽车环境中的多种语言使用者.
研究的目的:
- 为车载对话提出一个基于注意力的交互式对比学习框架 (IABCL).
- 增强汽车系统中的跨语言NLU能力.
- 在车载对话中解决跨语言互动的挑战.
主要方法:
- 在编码器中使用对比学习来提高跨语言理解,通过区分跨语言的类似含义.
- 在解码器中应用注意力机制来表达插槽和意图,增强语言家族内的NLU.
- 为评估建立了一个多语言车载对话 (MIvD) 数据集.
主要成果:
- 在跨语言对话任务中,IABCL框架表现得更好.
- 在意图识别方面取得了2.42%的改进.
- 与最新型号相比,显示了插槽填充1.43%的改进和2.67%的整体改进.
结论:
- IABCL框架有效地增强了车载对话系统的跨语言NLU.
- 拟议的方法为处理汽车通信中的语言多样性提供了一个强大的解决方案.
- IABCL显示了多语言智能车辆系统未来发展的巨大潜力.
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