多意图语音语言理解：方法、趋势与挑战
Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges
AI总结：
本文综述了多意图语音语言理解的方法、趋势与挑战，分析了现有模型的性能与局限性，并探讨了未来研究方向。
摘要：
多意图语音语言理解（SLU）涉及两个任务：多意图检测和槽位填充，共同处理包含多个意图的 utterances。由于这一特征，紧密反映了现实世界应用，该任务已吸引越来越多的研究关注，并取得了显著进展。然而，仍然缺乏对现有多意图 SLU 研究的全面和系统性综述。为此，本文介绍了最近多意图 SLU 的进展。我们从两种视角对先前研究进行了深入概述：解码范式和建模方法。在此基础上，我们进一步比较了代表性模型的性能，并分析了它们的优缺点。最后，我们讨论了当前的挑战，并概述了未来研究的有前途的方向。我们希望此次综述能提供有价值的见解，并为多意图 SLU 研究的进步提供有用的参考。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
