5 papers
MAC-SLU: Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark
Yuezhang Peng, Chonghao Cai, Ziang Liu +10
Spoken Language Understanding (SLU), which aims to extract user semantics to execute downstream tasks, is a crucial component of task-oriented dialog systems. Existing SLU datasets…
Ellipsoid-Based Decision Boundaries for Open Intent Classification
Yuetian Zou, Hanlei Zhang, Hua Xu +2
Textual open intent classification is crucial for real-world dialogue systems, enabling robust detection of unknown user intents without prior knowledge and contributing to the rob…
LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition
Qianrui Zhou, Hua Xu, Yifan Wang +2
Understanding human intents from multimodal signals is critical for analyzing human behaviors and enhancing human-machine interactions in real-world scenarios. However, existing me…
Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark
Hanlei Zhang, Zhuohang Li, Yeshuang Zhu +5
Multimodal language analysis is a rapidly evolving field that leverages multiple modalities to enhance the understanding of high-level semantics underlying human conversational utt…
Multimodal Classification and Out-of-distribution Detection for Multimodal Intent Understanding
Hanlei Zhang, Qianrui Zhou, Hua Xu +3
Multimodal intent understanding is a significant research area that requires effective leveraging of multiple modalities to analyze human language. Existing methods face two main c…