Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges
It addresses the lack of a systematic review for researchers in natural language processing and speech technology, but it is incremental as it summarizes existing work without introducing new methods.
This paper provides a comprehensive survey of recent advances in multi-intent spoken language understanding, covering decoding paradigms and modeling approaches, and compares the performance of representative models while analyzing their strengths and limitations.
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.