mlsanityData to 7 Oct 2026

Examining Spanish Counseling with MIDAS: a Motivational Interviewing Dataset in Spanish

arXiv 2502.08458 ↗First posted 12 Feb 2025cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 12 Feb 2025.

Builds on

8 of 38 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Supportcited by 92
  3. 2023Spanish Pre-trained BERT Model and Evaluation Datacited by 81
  4. 2024A Computational Framework for Behavioral Assessment of LLM Therapistscited by 61
  5. 2024Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactionscited by 43
  6. 2024LLM-based Conversational AI Therapist for Daily Functioning Screening and Psychotherapeutic Intervention via Everyday Smart Devicescited by 21
  7. 2021EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Predictioncited by 17
  8. 2021pysentimiento: A Python Toolkit for Opinion Mining and Social NLP taskscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.