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

Ano de publicación: 2024

Número: 73

Páxinas: 55-67

Tipo: Artigo

Outras publicacións en: Procesamiento del lenguaje natural

Resumo

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.

Referencias bibliográficas

  • Amjad, M., G. Sidorov, and A. Zhila. 2020. Data augmentation using machine translation for fake news detection in the Urdu language. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2537–2542, Marseille, France, May. European Language Resources Association.
  • Bansal, M. A., D. R. Sharma, and D. M. Kathuria. 2022. A systematic review on data scarcity problem in deep learning: solution and applications. ACM Computing Surveys (CSUR), 54(10s):1–29.
  • Bayer, M., M.-A. Kaufhold, and C. Reuter. 2022. A survey on data augmentation for text classification. ACM Computing Surveys, 55(7):1–39, dec.
  • Chapelle, O., B. Schölkopf, and A. Zien, editors. 2006. Semi-Supervised Learning. The MIT Press.
  • Crestani, F., D. E. Losada, and J. Parapar. 2022. Early risk prediction of mental health disorders. In Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project. Springer, pages 1–6.
  • Cui, X., V. Goel, and B. Kingsbury. 2014. Data augmentation for deep neural network acoustic modeling. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5582–5586.
  • DiClemente, C. C. and J. O. Prochaska. 1998. Toward a comprehensive, transtheoretical model of change: Stages of change and addictive behaviors.
  • Dinh, T. A., D. Liu, and J. Niehues. 2022. Tackling data scarcity in speech translation using zero-shot multilingual machine translation techniques. In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6222–6226.
  • Du, J., E. Grave, B. Gunel, V. Chaudhary, O. Celebi, M. Auli, V. Stoyanov, and A. Conneau. 2020. Self-training improves pre-training for natural language understanding.
  • He, J., J. Gu, J. Shen, and M. Ranzato. 2020. Revisiting self-training for neural sequence generation.
  • Hedderich, M. A., L. Lange, H. Adel, J. Strötgen, and D. Klakow. 2021. A survey on recent approaches for natural language processing in low-resource scenarios.
  • Hettema, J., J. Steele, and W. R. Miller. 2005. Motivational interviewing. Annual Review of Clinical Psychology, 1(1):91–111. PMID: 17716083.
  • Hoang, V. C. D., P. Koehn, G. Haffari, and T. Cohn. 2018. Iterative back-translation for neural machine translation. In Proceedings of the 2nd workshop on neural machine translation and generation, pages 18–24.
  • Huang, G., A. Gorin, J.-L. Gauvain, and L. Lamel. 2016. Machine translation based data augmentation for cantonese keyword spotting. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6020–6024.
  • Karamanolakis, G., S. Mukherjee, G. Zheng, and A. H. Awadallah. 2021. Self-training with weak supervision.
  • Ko, T., V. Peddinti, D. Povey, and S. Khudanpur. 2015. Audio augmentation for speech recognition. In Proc. Interspeech 2015, pages 3586–3589.
  • Koroteev, M. V. 2021. Bert: A review of applications in natural language processing and understanding.
  • Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2017. Imagenet classification with deep convolutional neural networks. Commun. ACM, 60(6):84–90, may.
  • Kwasnicka, D., S. U. Dombrowski, M. White, and F. Sniehotta. 2016. Theoretical explanations for maintenance of behaviour change: a systematic review of behaviour theories. Health psychology review, 10(3):277–296.
  • Laranjo, L., A. G. Dunn, H. L. Tong, A. B. Kocaballi, J. Chen, R. Bashir, D. Surian, B. Gallego, F. Magrabi, A. Y. S. Lau, and E. Coiera. 2018. Conversational agents in healthcare: a systematic review. Journal of the American Medical Informatics Association, 25(9):1248–1258, 07.
  • Lee, D.-H. 2013. Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks.
  • Liu, X., F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang. 2021. Selfsupervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, pages 1–1.
  • Medvedev, A. N., R. Lambiotte, and J.-C. Delvenne. 2019. The anatomy of reddit: An overview of academic research. In F. Ghanbarnejad, R. Saha Roy, F. Karimi, J.-C. Delvenne, and B. Mitra, editors, Dynamics On and Of Complex Networks III, pages 183–204, Cham. Springer International Publishing.
  • Meyer, S. and D. Elsweiler. 2022. GLo-HBCD: A naturalistic German dataset for language of health behaviour change on online support forums. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2226–2235, Marseille, France, June. European Language Resources Association.
  • Meyer, S. and D. Elsweiler. 2023. Towards cross-content conversational agents for behaviour change: Investigating domain independence and the role of lexical features in written language around change. In Proceedings of the 5th International Conference on Conversational User Interfaces, CUI ’23, New York, NY, USA. Association for Computing Machinery.
  • Meyer, S., D. Elsweiler, B. Ludwig, M. Fern´andez-Pichel, and D. Losada. 2022. Do we still need human assessors? prompt-based gpt-3 user simulation in conversational ai. 07.
  • Miller, W. and S. Rollnick. 2003. Motivational interviewing: Preparing people for change, 2nd ed. Journal For Healthcare Quality, 25:46, 05.
  • Mukherjee, S. and A. Awadallah. 2020. Uncertainty-aware self-training for fewshot text classification. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 21199–21212. Curran Associates, Inc.
  • Olafsson, S., T. O’Leary, and T. Bickmore. 2019. Coerced change-talk with conversational agents promotes confidence in behavior change. In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth’19, page 31–40, New York, NY, USA. Association for Computing Machinery.
  • Ríssola, E. A., D. E. Losada, and F. Crestani. 2021. A survey of computational methods for online mental state assessment on social media. ACM Trans. Comput. Healthcare, 2(2), mar.
  • Rubak, S., A. Sandbæk, T. Lauritzen, and B. Christensen. 2005. Motivational interviewing: a systematic review and metaanalysis. British journal of general practice, 55(513):305–312.
  • Schulman, D., T. Bickmore, and C. Sidner. 2011. An intelligent conversational agent for promoting long-term health behavior change using motivational interviewing. 01.
  • Shen, J. H. and F. Rudzicz. 2017. Detecting anxiety through Reddit. In Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality, pages 58–65, Vancouver, BC, August. Association for Computational Linguistics.
  • Shim, H., S. Luca, D. Lowet, and B. Vanrumste. 2020. Data augmentation and semi-supervised learning for deep neural networks-based text classifier. In Proceedings of the 35th Annual ACM Symposium on Applied Computing, SAC ’20, page 1119–1126, New York, NY, USA. Association for Computing Machinery.
  • Smriti, D., J. Y. Shin, M. Mujib, M. Colosimo, T.-S. Kao, J. Williams, and J. Huh-Yoo. 2021. Tamica: Tailorable autonomous motivational interviewing conversational agent. In Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth ’20, page 411–414, New York, NY, USA. Association for Computing Machinery.
  • Tadesse, M. M., H. Lin, B. Xu, and L. Yang. 2019. Detection of depression-related posts in reddit social media forum. IEEE Access, 7:44883–44893.
  • Tiedemann, J. and S. Thottingal. 2020. OPUS-MT – building open translation services for the world. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 479–480, Lisboa, Portugal, November. European Association for Machine Translation.
  • Varshney, N., S. Mishra, and C. Baral. 2021. Interviewer-candidate role play: Towards developing real-world nlp systems.
  • Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin. 2017. Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
  • Wei, C., K. Shen, Y. Chen, and T. Ma. 2022. Theoretical analysis of self-training with deep networks on unlabeled data. Xie, Q., Z. Dai, E. Hovy, M.-T. Luong, and Q. V. Le. 2020. Unsupervised data augmentation for consistency training.
  • Yu, A. W., D. Dohan, M.-T. Luong, R. Zhao, K. Chen, M. Norouzi, and Q. V. Le. 2018. Qanet: Combining local convolution with global self-attention for reading comprehension.
  • Yu, Y., S. Zuo, H. Jiang, W. Ren, T. Zhao, and C. Zhang. 2021. Fine-tuning pretrained language model with weak supervision: A contrastive-regularized selftraining approach.