Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges

TL;DR

This survey reviews recent advances in multi-intent spoken language understanding, focusing on decoding paradigms and modeling approaches, analyzing their strengths and limitations, and discussing future research challenges.

Contribution

It provides a comprehensive systematic review of existing studies on multi-intent SLU, highlighting recent progress, model comparisons, and future research directions.

Findings

Performance varies across different models and approaches.

Decoding paradigms significantly impact SLU effectiveness.

Current challenges include handling complex multi-intent utterances.

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.