DialogGraph-LLM: Graph-Informed LLMs for End-to-End Audio Dialogue Intent Recognition
Abstract
Recognizing speaker intent in long audio dialogues among speakers has a wide range of applications, but is a non-trivial AI task due to complex inter-dependencies in speaker utterances and scarce annotated data. To address these challenges, an end-to-end framework, namely DialogGraph-LLM, is proposed in the current work. DialogGraph-LLM combines a novel Multi-Relational Dialogue Attention Network (MR-DAN) architecture with multimodal foundation models (e.g., Qwen2.5-Omni-7B) for direct acoustic-to-intent inference. An adaptive semi-supervised learning strategy is designed using LLM with a confidence-aware pseudo-label generation mechanism based on dual-threshold filtering using both global and class confidences, and an entropy-based sample selection process that prioritizes high-information unlabeled instances. Extensive evaluations on the proprietary MarketCalls corpus and the publicly available MIntRec 2.0 benchmark demonstrate DialogGraph-LLM's superiority over strong audio and text-driven baselines. The framework demonstrates strong performance and efficiency in intent recognition in real world scenario audio dialogues, proving its practical value for audio-rich domains with limited supervision. Our code is available at https://github.com/david188888/DialogGraph-LLM.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents DialogGraph-LLM, a novel framework for audio dialogue intent recognition that combines graph-based modeling with advanced semi-supervised learning techniques, demonstrating substantial improvements over existing methods. The comprehensive methodology and robust experimental validation position this work as a meaningful contribution to the field of machine learning, particularly in audio processing and dialogue understanding.
Methodology
The proposed DialogGraph-LLM framework introduces a novel Multi-Relational Dialogue Attention Network (MR-DAN) that effectively models complex inter-dependencies in audio dialogues through a graph-based approach. The integration of multimodal foundation models with a confidence-aware semi-supervised learning strategy enhances the model's ability to infer intent from raw audio data, addressing the challenges of limited annotated datasets. The methodology is well-structured, with clear definitions of edge types and attention mechanisms tailored to the unique properties of dialogue data.
Results & evaluation
The experiments are comprehensive, utilizing both proprietary and publicly available datasets (MarketCalls and MIntRec 2.0) to evaluate the performance of DialogGraph-LLM against strong baselines. The reported results demonstrate significant improvements in accuracy and F1 scores, particularly highlighting the effectiveness of the MR-DAN and the adaptive semi-supervised learning strategy. The ablation studies further validate the contributions of individual components, reinforcing the robustness of the proposed model.
Reproducibility
The paper provides sufficient detail regarding the experimental setup, including dataset descriptions, model configurations, and evaluation metrics, which supports reproducibility. The availability of the code repository on GitHub enhances the potential for other researchers to replicate and build upon the work.
Limitations
One limitation noted is the reliance on a specific LLM backbone (Qwen2.5-Omni-7B) without exploring the performance across different LLM architectures. Additionally, the proposed adaptive semi-supervised learning strategy may still be susceptible to noise propagation from pseudo-labels, which could affect model performance in practice.
Broader impact
The framework has significant implications for applications in human-computer interaction, customer service, and other audio-rich domains where intent recognition is crucial. By effectively leveraging audio data and addressing the challenges of limited supervision, DialogGraph-LLM could enhance the capabilities of dialogue systems in real-world scenarios. The paper presents DialogGraph-LLM, a novel framework for audio dialogue intent recognition that combines graph-based modeling with advanced semi-supervised learning techniques, demonstrating substantial improvements over existing methods. The comprehensive methodology and robust experimental validation position this work as a meaningful contribution to the field of machine learning, particularly in audio processing and dialogue understanding.
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