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Enhanced Emotion and Sentiment Recognition for Empathetic Dialogue System Using Big Data and Deep Learning Methods

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Computational Science – ICCS 2023 (ICCS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14073))

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Abstract

The article presents the results of work on improving sentiment and emotion recognition for Polish texts using a big data-based expansion process and larger neural language models. The proposed recognition method is intended to serve in a therapeutic dialogue system to analyze sentiment and emotion in human utterances. First, the language model is enhanced, by replacing the BERT neural language model with RoBERTa. Next, the emotion-based text corpus is enlarged. A novel process of augmenting an emotion-labeled text corpus using semantically similar data from an unlabeled corpus, inspired by semi-supervised learning methods, is proposed. The process of using the Common Crawl web archive to create an enlarged corpus, named CORTEX+pCC, is presented. An empathetic dialogue system named Terabot, incorporating the elaborated method, is also described. The system is designed to employ elements of cognitive-behavioral therapy for psychiatric patients. The improved language model trained on the enlarged CORTEX+pCC corpus resulted in remarkably improved sentiment and emotion recognition. The average accuracy and F1 scores increased by around 3% and 8% relative, which will allow the dialogue system to operate more appropriately for the emotional state of the patient.

This research was funded by the Center for Priority Research Area Artificial Intelligence and Robotics of the Warsaw University of Technology within the Excellence Initiative: Research University (IDUB) program.

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Notes

  1. 1.

    https://rasa.com/products/rasa-platform/.

  2. 2.

    CORTEX is freely available for download at https://github.com/azygadlo/CORTEX.

  3. 3.

    http://commoncrawl.org/.

  4. 4.

    https://www.loc.gov/preservation/digital/formats/fdd/fdd000236.shtml.

  5. 5.

    https://commoncrawl.org/the-data/get-started.

  6. 6.

    https://www.sbert.net.

References

  1. Bommasani, R., et al.: On the opportunities and risks of foundation models. CoRR (2021). https://doi.org/10.48550/arXiv.2108.07258

  2. Brown, T., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901 (2020)

    Google Scholar 

  3. Bunk, T., Varshneya, D., Vlasov, V., Nichol, A.: Diet: lightweight language understanding for dialogue systems (2020). https://doi.org/10.48550/arXiv.2004.09936

  4. Carroll, K., Rounsaville, B.: Computer-assisted therapy in psychiatry: be brave-it’s a new world. Curr. Psychiatry Rep. 12, 426–432 (2010). https://doi.org/10.1007/s11920-010-0146-2

    Article  Google Scholar 

  5. Chen, Q., Zhuo, Z., Wang, W.: BERT for joint intent classification and slot filling. arXiv preprint arXiv:1902.10909 (2019)

  6. Craig, T.K., et al.: Avatar therapy for auditory verbal hallucinations in people with psychosis: a single-blind, randomised controlled trial. Lancet Psychiatry 5(1), 31–40 (2018). https://doi.org/10.1016/S2215-0366(17)30427-3

    Article  Google Scholar 

  7. Crouse, S., Nagel, S., Elbaz, G., Malamud, C.: Common Crawl Foundation. http://commoncrawl.org (2008)

  8. Dadas, S., Perełkiewicz, M., Poświata, R.: Pre-training polish transformer-based language models at scale. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2020. LNCS (LNAI), vol. 12416, pp. 301–314. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61534-5_27

    Chapter  Google Scholar 

  9. Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-XL: attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860 (2019)

  10. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  11. Dino, F., Zandie, R., Abdollahi, H., Schoeder, S., Mahoor, M.H.: Delivering cognitive behavioral therapy using a conversational social robot. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2089–2095 (2019). https://doi.org/10.1109/IROS40897.2019.8968576

  12. Fernández-Caballero, A., et al.: Human-avatar symbiosis for the treatment of auditory verbal hallucinations in schizophrenia through virtual/augmented reality and brain-computer interfaces. Front. Neuroinform. 11 (2017). https://doi.org/10.3389/fninf.2017.00064

  13. Fitzpatrick, K.K., Darcy, A., Vierhile, M.: Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment Health 4(2), e19 (2017). https://doi.org/10.2196/mental.7785. http://mental.jmir.org/2017/2/e19/

  14. Jiao, A.: An intelligent chatbot system based on entity extraction using RASA NLU and neural network. In: Journal of Physics: Conference Series, vol. 1487, no. 1, p. 012014 (2020). https://doi.org/10.1088/1742-6596/1487/1/012014

  15. Kitaev, N., Kaiser, Ł., Levskaya, A.: Reformer: the efficient transformer. arXiv preprint arXiv:2001.04451 (2020)

  16. Li, Y., Su, H., Shen, X., Li, W., Cao, Z., Niu, S.: DailyDialog: a manually labelled multi-turn dialogue dataset. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing, Taipei, Taiwan (Volume 1: Long Papers), pp. 986–995. Asian Federation of Natural Language Processing (2017). https://www.aclweb.org/anthology/I17-1099

  17. Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

  18. Mühleisen, H., Bizer, C.: Web data commons-extracting structured data from two large web corpora. LDOW 937, 133–145 (2012)

    Google Scholar 

  19. Oliver, A., Odena, A., Raffel, C.A., Cubuk, E.D., Goodfellow, I.: Realistic evaluation of deep semi-supervised learning algorithms. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

  20. Pudo, M., Szczepanek, N., Lukasiak, B., Janicki, A.: Semi-supervised learning with limited data for automatic speech recognition. In: Proceedings of the IEEE 7th Forum on Research and Technologies for Society and Industry Innovation (RTSI 2022), Paris, France, pp. 136–141 (2022). https://doi.org/10.1109/RTSI55261.2022.9905112

  21. Rashkin, H., Smith, E.M., Li, M., Boureau, Y.L.: Towards empathetic open-domain conversation models: a new benchmark and dataset. arXiv preprint arXiv:1811.00207 (2018)

  22. Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. arXiv preprint arXiv:1908.10084 (2019)

  23. Roziewski, S., Kozłowski, M.: LanguageCrawl: a generic tool for building language models upon Common Crawl. Lang. Resour. Eval. 55(4), 1047–1075 (2021)

    Article  Google Scholar 

  24. Sharma, S., He, J., Suleman, K., Schulz, H., Bachman, P.: Natural language generation in dialogue using lexicalized and delexicalized data. In: International Conference on Learning Representations: Workshop (2017)

    Google Scholar 

  25. Smith, J.R., Saint-Amand, H., Plamada, M., Koehn, P., Callison-Burch, C., Lopez, A.: Dirt cheap web-scale parallel text from the common crawl. In: ACL (1), pp. 1374–1383 (2013)

    Google Scholar 

  26. Stefaniak, I., Sorokosz, K., Janicki, A., Wciórka, J.: Therapy based on avatar-therapist synergy for patients with chronic auditory hallucinations: a pilot study. Schizophr. Res. 211, 115–117 (2019). https://doi.org/10.1016/j.schres.2019.05.036. https://www.sciencedirect.com/science/article/pii/S0920996419302130

  27. Su, P.H., et al.: On-line active reward learning for policy optimisation in spoken dialogue systems. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2431–2441 (2016)

    Google Scholar 

  28. Tamkin, A., Brundage, M., Clark, J., Ganguli, D.: Understanding the capabilities, limitations, and societal impact of large language models. CoRR (2021). https://doi.org/10.48550/arXiv.2102.02503

  29. Vaidyam, A.N., Wisniewski, H., Halamka, J.D., Kashavan, M.S., Torous, J.B.: Chatbots and conversational agents in mental health: a review of the psychiatric landscape. Can. J. Psychiatry 64(7), 456–464 (2019)

    Article  Google Scholar 

  30. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  31. Vlasov, V., Mosig, J.E.M., Nichol, A.: Dialogue transformers (2019). https://doi.org/10.48550/arXiv.1910.00486

  32. Weidinger, L., et al.: Ethical and social risks of harm from language models. CoRR (2021). https://doi.org/10.48550/arXiv.2112.04359

  33. World Health Organization, et al.: The WHO special initiative for mental health (2019–2023): universal health coverage for mental health. Technical report, World Health Organization (2019)

    Google Scholar 

  34. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  35. Zhong, V., Xiong, C., Socher, R.: Global-locally self-attentive dialogue state tracker. In: Association for Computational Linguistics (2018)

    Google Scholar 

  36. Zygadło, A., Kozłowski, M., Janicki, A.: Text-based emotion recognition in English and Polish for therapeutic chatbot. Appl. Sci. 11(21), 10146 (2021)

    Article  Google Scholar 

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Authors and Affiliations

  1. Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marek Kozłowski, Karolina Gabor-Siatkowska, Marcin Sowański & Artur Janicki

  2. Institute of Psychiatry and Neurology, ul. Sobieskiego 9, 02-957, Warsaw, Poland

    Izabela Stefaniak

Authors
  1. Marek Kozłowski
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  2. Karolina Gabor-Siatkowska
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  3. Izabela Stefaniak
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  4. Marcin Sowański
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  5. Artur Janicki
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Corresponding authors

Correspondence to Marek Kozłowski or Artur Janicki .

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Editors and Affiliations

  1. Czech Technical University in Prague, Prague, Czech Republic

    Jiří Mikyška

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Kozłowski, M., Gabor-Siatkowska, K., Stefaniak, I., Sowański, M., Janicki, A. (2023). Enhanced Emotion and Sentiment Recognition for Empathetic Dialogue System Using Big Data and Deep Learning Methods. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14073. Springer, Cham. https://doi.org/10.1007/978-3-031-35995-8_33

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