M3TCM：用于动机访谈中话语分类的多模态多任务上下文模型
M3TCM: Multi-modal Multi-task Context Model for Utterance Classification in Motivational Interviews
AI总结：
提出 M3TCM，通过多任务学习融合文本、语音与上下文，建模来访者和治疗师行为，在 AnnoMI 上显著提升动机访谈话语分类性能。
摘要：
在动机访谈中，准确的话语分类对于自动理解来访者与治疗师互动的质量和动态至关重要，并且可以作为调解此类互动的系统的关键输入。动机访谈表现出三个重要特征。第一，存在两个不同角色，即来访者和治疗师。第二，这些访谈通常带有高度情绪色彩，而情绪可以同时通过文本和韵律表达。最后，上下文对于分类任何给定话语都具有核心重要性。以往工作未能将所有这些特征充分纳入心理健康对话的话语分类方法。相比之下，我们提出 M3TCM，这是一种用于话语分类的多模态、多任务上下文模型。我们的方法首次采用多任务学习，以有效建模治疗师与来访者行为的联合成分和个体成分。此外，M3TCM 整合来自文本和语音模态以及对话上下文的信息。凭借这一新方法，我们在最近推出的 AnnoMI 数据集上超越了话语分类的当前最优水平：来访者话语分类相对提升 20%，治疗师话语分类相对提升 15%。在大量消融研究中，我们量化了每项贡献所带来的改进。
英文摘要：
Accurate utterance classification in motivational interviews is crucial to automatically understand the quality and dynamics of client-therapist interaction, and it can serve as a key input for systems mediating such interactions. Motivational interviews exhibit three important characteristics. First, there are two distinct roles, namely client and therapist. Second, they are often highly emotionally charged, which can be expressed both in text and in prosody. Finally, context is of central importance to classify any given utterance. Previous works did not adequately incorporate all of these characteristics into utterance classification approaches for mental health dialogues. In contrast, we present M3TCM, a Multi-modal, Multi-task Context Model for utterance classification. Our approach for the first time employs multi-task learning to effectively model both joint and individual components of therapist and client behaviour. Furthermore, M3TCM integrates information from the text and speech modality as well as the conversation context. With our novel approach, we outperform the state of the art for utterance classification on the recently introduced AnnoMI dataset with a relative improvement of 20% for the client- and by 15% for therapist utterance classification. In extensive ablation studies, we quantify the improvement resulting from each contribution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
