Examining S panish Counseling with MIDAS : a Motivational Interviewing Dataset in S panish (2025)

Abstract: Cultural and language factors significantly influence counseling, but Natural Language Processing research has not yet examined whether the findings of conversational analysis for counseling conducted in English apply to other languages. This paper presents a first step towards this direction. We introduce MIDAS (Motivational Interviewing Dataset in Spanish), a counseling dataset created from public video sources that contains expert annotations for counseling reflections and questions. Using this dataset, we explore language-based differences in counselor behavior in English and Spanish and develop classifiers in monolingual and multilingual settings, demonstrating its applications in counselor behavioral coding tasks.

## introduction a growing number of natural language processing (nlp) research studies focus on mental and behavioral health issues, covering applications such as building automated chatbots to simulate counselors @xcite @xcite , monitoring patients' mental states @xcite , or building feedback systems to aid counselor training @xcite @xcite . although this body of work seeks to address the growing need for mental health support around the world, the majority of it has only focused on english. this can be partially attributed to the lack of counseling datasets in other languages, which are difficult to obtain due to the private nature of counseling interactions and the need for expert annotations. patients seeking mental health care struggle to find adequate resources, especially when they are not native speakers @xcite . studies * equal contribution. in clinical psychotherapy have shown that cultural differences between patients and providers can lead to disparities in quality of mental health care due to unsuccessful interactions @xcite . this highlights the importance of collecting and using culturally diverse counseling datasets when developing nlp-based tools that support counseling practice. in this study, we introduce midas (motivational interviewing dataset in spanish), a new dataset of spanish counseling conversations conducted using motivational interviewing (mi), a counseling style that focuses on eliciting patients' motivation to change @xcite . we use midas to explore the differences in conversational strategies used by spanish and english mi counselors. we also conduct classification experiments to classify counselor behaviors using monolingual and multilingual models. our results show that models trained on spanish data outperform those trained on english, highlighting the need for language-specific datasets in psychotherapy research. ## related work the language used in counseling varies based on the demographic and cultural background of both counselors and patients @xcite , underscoring the importance of considering diversity in user identities when designing nlp systems for mental health. despite growing interest in developing nlp methods for understanding counseling conversations, very few non-english datasets are publicly available, further limiting nlp research in multilingual mental healthcare. globhcd @xcite is a german dataset with naturalistic interactions around changing health behavior. the interactions were obtained from participants in an online mental health forum and annotated with mi labels. although the code to replicate the dataset is available, the annotated dataset is not publicly available. bimisc is a dutch dataset that contains bilingual mi conversations manually annotated with counselor and client behaviors @xcite . similarly, @xcite collected a dataset of real conversations between patients and mental health counselors and annotated the conversations with behavioral codes based on the contribution of the speaker. the broader landscape of mental health applications for non-english nlp contains a larger body of work. social media and text communication platforms are popular avenues for sourcing data. the chinese psyqa dataset contains annotated question-answer pairs from an online mental health service @xcite . the hing-poem dataset in hinglish examines politeness in mental health and legal counseling conversations @xcite , and research on interactions in kenyan whatsapp groups for peer support studies sentiment among youth living with hiv @xcite . additionally, previous work has sourced data from social media for mental illness prediction @xcite . an alternative to direct data collection is to use machine translation from high-resource to lowresource languages @xcite , but this comes with the potential cost of cultural information loss. our study introduces the first spanish mi dataset, filling a critical gap in the literature and offering a valuable resource for nlp researchers working on mental health applications. ## analyzing conversational strategies of spanish-speaking counselors we explore culture-specific strategies that spanishspeaking counselors use in mi-style counseling by conducting language-based comparisons against mi counseling in english. we focus on conversational aspects previously identified as relevant for counseling quality, such as conversational dynamics, language use, and sentiment expressed during conversations @xcite . during our analyses, we use an english counseling dataset @xcite compiled with the same methodology as our spanish dataset. it includes labels for counselor quality (low and high), as well as annotations for questions and reflections. our analysis uses the 72 high-quality sessions available in the dataset. on an important note, although our dataset lacks evaluations of counseling proficiency, we assume that counselors exhibit desirable behaviors during conversations, designed to show mi skills. we instead use the reflection-to-question ratio (r:q) as a proficiency indicator @xcite . the resulting small difference between the average ratios (0.59 for spanish, 0.64 for english) suggests that the spanish mi counselors in midas have proficiency levels in mi similar to the counselors represented in the english dataset. ## predicting counselor behaviors in addition to linguistic analyzes, we perform classification experiments in spanish and english conversations to classify counselor behavior using mi-das and its english counterpart, described in section 4. similarly to the label classification experiments in @xcite , we define two tasks: binary classification to differentiate reflections from questions, and three-way classification to identify questions, reflections, or neither. we experiment with two settings: we train and test the classifiers using the same language for both the training and the test data; and we use multilingual language models to enable training on one language and evaluation on the other. for our experiments, we use a 85%-15% training-test split. for the monolingual experiments, as our main models we use bert @xcite and beto @xcite , a bert architecture trained on spanish text. for the multilingual experiments, we use a bert architecture trained for multiple languages, including english and spanish bert @xcite , denoted as ml-bert. we attach classification heads to the base models and fine-tune each model for five epochs each. results for the classification experiments are shown in in general, we observe that questions are easier to predict than reflections. this aligns with previous work done on english, where reflections were also more challenging to classify, and with work conducted on hebrew @xcite in which questions are easier to classify than other codes. an important take-away from our experiments is that performing training and evaluation in the same language outperforms multilingual settings. ## conclusion in this work, we introduced midas, a motivational interviewing dataset in spanish, the first spanish mi dataset. we conducted comparative analyzes of the language used by counselors in spanish and english counseling interactions and found differences in linguistic styles and conversation dynamics. future work includes a more ex-tensive analysis of the differences between english and spanish counseling, including conversational dynamics such as verbal mirroring and power dynamics, as well as conversational strategies such as empathy or partnership. we also envision midas as a valuable resource in building nlp applications to support counseling evaluation and training for spanish speakers. the midas dataset is publicly available under https://github.com/michigannlp/midas .