系统梳理多意图语音理解研究进展与挑战
Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges
- 从解码范式与建模方法两方面分析多意图语音理解技术
- 对比主流模型性能，揭示各自优缺点
- 适合语音交互、智能助手等方向的研究者参考
多意图语音语言理解（Multi-intent SLU）包含多意图检测与槽位填充两个任务，共同处理包含多个意图的语句。由于该任务更贴近真实应用场景，近年来受到越来越多关注，并取得显著进展。然而，现有研究缺乏对多意图SLU的全面系统性综述。为此，本文对近期多意图SLU进展进行系统调研，从解码范式和建模方法两个角度深入剖析已有研究；在此基础上，对比代表性模型的性能，分析其优势与局限；最后，讨论当前面临的挑战，并展望未来有潜力的研究方向。希望本综述能为推进多意图SLU研究提供有益参考。
原文摘要 · Abstract (English)
Multi-intent spoken language understanding (SLU) involves two tasks: multiple intent detection and slot filling, which jointly handle utterances containing more than one intent. Owing to this characteristic, which closely reflects real-world applications, the task has attracted increasing research attention, and substantial progress has been achieved. However, there remains a lack of a comprehensive and systematic review of existing studies on multi-intent SLU. To this end, this paper presents a survey of recent advances in multi-intent SLU. We provide an in-depth overview of previous research from two perspectives: decoding paradigms and modeling approaches. On this basis, we further compare the performance of representative models and analyze their strengths and limitations. Finally, we discuss the current challenges and outline promising directions for future research. We hope this survey will offer valuable insights and serve as a useful reference for advancing research in multi-intent SLU.
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