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.

翻译：在动机性访谈中，准确的话语分类对于自动理解来访者与治疗师互动质量及动态过程至关重要，并可作为此类互动中介系统的关键输入。动机性访谈具有三个重要特征：首先，存在两种截然不同的角色，即来访者与治疗师；其次，对话常带有强烈的情感色彩，这种情感可通过文本和韵律两种方式表达；最后，上下文语境对于任何话语的分类都至关重要。以往的研究未能充分将这些特征纳入心理健康对话的话语分类方法中。为此，我们提出M3TCM——一种面向话语分类的多模态多任务上下文模型。该方法首次采用多任务学习，有效建模治疗师与来访者行为的联合成分与独立成分。此外，M3TCM整合了文本与语音模态信息以及对话上下文。通过这一新颖方法，我们在最新发布的AnnoMI数据集上实现了话语分类的最优性能，来访者与治疗师话语分类分别相对提升20%和15%。通过广泛的消融实验，我们量化了各项贡献所带来的性能改进。