Semi-Supervised Learning in the Field of Conversational Agents and Motivational Interviewing

  1. Rosenova, Gergana
  2. Fernández-Pichel, Marcos
  3. Meyer, Selina
  4. Losada, David E.
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2024

Número: 73

Páginas: 55-67

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

La explotación de los conceptos de la Entrevista Motivacional para el análisis de texto contribuye a obtener valiosas lecciones sobre las actitudes y perspectivas de los individuos hacia el cambio de comportamiento. La escasez de datos de usuario etiquetados plantea un desafío continuo e impide avances técnicos en la investigación bajo escenarios de idiomas no ingleses. Para abordar las limitaciones del etiquetado manual de datos, proponemos un método de aprendizaje semisupervisado como medio para aumentar un corpus de entrenamiento existente. Nuestro enfoque aprovecha los datos generados por usuarios obtenidos de comunidades en redes sociales y usando traducción automática y emplea técnicas de autoentrenamiento para la asignación de etiquetas. Con este fin, consideramos varias fuentes y llevamos a cabo una evaluación de múltiples clasificadores entrenados en varios conjuntos de datos aumentados. Los resultados indican que este enfoque de etiquetado débil no produce mejoras en las capacidades de clasificación generales de los modelos. Sin embargo, se observaron mejoras notables para las clases minoritarias. Concluimos que varios factores, incluida la calidad de la traducción automática, pueden potencialmente sesgar los modelos de pseudoetiquetado y que la naturaleza desequilibrada de los datos y el impacto de un umbral de pre-filtrado estricto deben tenerse en cuenta como factores inhibidores del rendimiento.

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Fuente de los datos: Dialnet