Abstract
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.
Abstract (translated)
多意图口语理解（SLU）涉及两个任务：多意图检测和槽填充，这些任务共同处理包含多个意图的语音输入。由于这一特性紧密反映了现实世界的应用场景，该任务吸引了越来越多的研究关注，并取得了实质性的进展。然而，目前尚缺乏对现有研究进行全面且系统的回顾。为此，本文介绍了近期在多意图SLU方面的研究成果综述。我们从两个视角——解码范式和建模方法——深入概述了以往的研究成果。在此基础上，我们进一步比较代表性模型的性能，并分析它们的优势与局限性。最后，我们讨论当前面临的挑战并展望未来研究的潜在方向。希望这项综述能够提供有价值的见解，并作为推进多意图SLU领域研究的重要参考。
URL
https://arxiv.org/abs/2512.11258
