A BiRGAT Model for Multi-intent Spoken Language Understanding with Hierarchical Semantic Frames
Previous work on spoken language understanding (SLU) mainly focuses on single-intent settings, where each input utterance merely contains one user intent. This configuration significantly limits the surface form of user utterances and the capacity of output semantics. In this work, we first propose a Multi-Intent dataset which is collected from a realistic in-Vehicle dialogue System, called MIVS. The target semantic frame is organized in a 3-layer hierarchical structure to tackle the alignment and assignment problems in multi-intent cases. Accordingly, we devise a BiRGAT model to encode the hierarchy of ontology items, the backbone of which is a dual relational graph attention network. Coupled with the 3-way pointer-generator decoder, our method outperforms traditional sequence labeling and classification-based schemes by a large margin.
Code (1)
Tasks
DecoderGraph AttentionSpoken Language UnderstandingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding
Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and slots). In this work, we introduce OpenS…
Spoken Language UnderstandingSFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding
Prompt-based spoken language understanding (SLU) with large language models (LLMs) often suffers from inconsistent intent--slot structures due to decoding stochasticity, particularly in multi-intent scenarios. In view of…
Spoken Language UnderstandingMulti-Intent Spoken Language Understanding: Methods, Trends, and Challenges
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 closel…
Spoken Language UnderstandingIntent DetectionSlot FillingA Unified Framework for Multi-intent Spoken Language Understanding with prompting
Multi-intent Spoken Language Understanding has great potential for widespread implementation. Jointly modeling Intent Detection and Slot Filling in it provides a channel to exploit the correlation between intents and slo…
Intent DetectionMulti-Task Learningslot-fillingSlot Filling+1Transferring SLU Models in Novel Domains
Spoken language understanding (SLU) is a critical component in building dialogue systems. When building models for novel natural language domains, a major challenge is the lack of data in the new domains, no matter wheth…
Intent RecognitionMeta-Learningslot-fillingSlot Filling+2