Abstract
The prevalence of mental disorders is increasing globally, highlighting the need for effective emotional support systems. Emotional Support Conversation (ESC) systems have emerged as a promising solution, providing supportive responses to individuals experiencing emotional distress. However, existing ESC systems face challenges in deducing appropriate support strategies with motivations and modeling complex semantic patterns inherent in support strategies. To tackle these challenges, we propose MAGIC, a Memory-enhanced emotional support conversation system with motivation-driven strAteGy InferenCe. Considering potential responses in future conversations, we instruct models to deduce motivations for selecting appropriate future strategies from the prior dialogue history, by harnessing the knowledge deducing abilities of Large Language Models (LLMs). These deduced motivations serve as chain-of-thought to steer models in understanding the underlying reasons of strategy inference. Moreover, to capture the intricate human language patterns and knowledge embedded in support strategies, we introduce a strategy memory store to enhance strategy modeling, by disentangling the representations from the same-strategy responses as strategy memory. Experimental results on the ESConv dataset demonstrate that MAGIC significantly outperforms state-of-the-art baselines in both automatic and human evaluations, showcasing its effectiveness in generating empathetic and supportive responses. Our codes are available at Github: https://github.com/Wh2018/MAGIC
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Banerjee, S., Lavie, A.: Meteor: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pp. 65–72 (2005)
Burleson, B.R.: Emotional support skills. In: Handbook of Communication and Social Interaction Skills, pp. 569–612. Routledge (2003)
Chen, M., et al.: The future of cognitive strategy-enhanced persuasive dialogue agents: New perspectives and trends. arXiv preprint arXiv:2402.04631 (2024)
Cheng, Y., et al.: Improving multi-turn emotional support dialogue generation with lookahead strategy planning. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 3014–3026 (2022)
Collaborators, G.M.D., et al.: Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet Psych. 9(2), 137–150 (2022)
Deng, Y., Zhang, W., Yuan, Y., Lam, W.: Knowledge-enhanced mixed-initiative dialogue system for emotional support conversations. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4079–4095 (2023)
Ghosal, D., Shen, S., Majumder, N., Mihalcea, R., Poria, S.: Cicero: a dataset for contextualized commonsense inference in dialogues. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5010–5028 (2022)
Guo, B.: Conditional text generation for harmonious human-machine interaction. ACM Trans. Intell. Syst. Technol. (TIST) 12(2), 1–50 (2021)
Heaney, C.A., Israel, B.A.: Social networks and social support. Health Beh. Health Educ. Theory, Res. Pract. 4(1), 189–210 (2008)
Hill, C.E.: Helping Skills: Facilitating, Exploration, Insight, and Action. American Psychological Association (2009)
Hu, E.J., et al.: Lora: low-rank adaptation of large language models. In: International Conference on Learning Representations (2021)
Jia, M., Chen, Q., Jing, L., Fu, D., Li, R.: Knowledge-enhanced memory model for emotional support conversation. arXiv preprint arXiv:2310.07700 (2023)
Kang, D., et al.: Can large language models be good emotional supporter? Mitigating preference bias on emotional support conversation. arXiv preprint arXiv:2402.13211 (2024)
Lewis, M., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871–7880 (2020)
Li, Q., Chen, H., Ren, Z., Ren, P., Tu, Z., Chen, Z.: EMPDG: multi-resolution interactive empathetic dialogue generation. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4454–4466 (2020)
Liang, Y., Liu, L., Ji, Y., Huangfu, L., Zeng, D.D.: Identifying emotional causes of mental disorders from social media for effective intervention. Inf. Process. Manag. 60(4), 103407 (2023)
Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004)
Liu, S., et al.: Towards emotional support dialog systems. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 3469–3483 (2021)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2018)
Majumder, N., et al.: Mime: mimicking emotions for empathetic response generation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8968–8979 (2020)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp. 311–318 (2002)
Peng, W., Hu, Y., Xing, L., Xie, Y., Sun, Y., Li, Y.: Control globally, understand locally: a global-to-local hierarchical graph network for emotional support conversation. In: International Joint Conference on Artificial Intelligence, pp. 4324–4330 (2022)
Peng, W., Qin, Z., Hu, Y., Xie, Y., Li, Y.: FADO: feedback-aware double controlling network for emotional support conversation. Knowl.-Based Syst. 264, 110340 (2023)
Pérez-Rosas, V., Mihalcea, R., Resnicow, K., Singh, S., An, L.: Understanding and predicting empathic behavior in counseling therapy. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1426–1435 (2017)
Rashkin, H., Smith, E.M., Li, M., Boureau, Y.L.: Towards empathetic open-domain conversation models: a new benchmark and dataset, pp. 5370–5381. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019)
Schulman, J., et al.: ChatGPT: optimizing language models for dialogue. OpenAI blog (2022)
Touvron, H., et al.: Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)
Tu, Q., Li, Y., Cui, J., Wang, B., Wen, J.R., Yan, R.: MISC: a mixed strategy-aware model integrating comet for emotional support conversation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 308–319 (2022)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Vedantam, R., Lawrence Zitnick, C., Parikh, D.: Cider: consensus-based image description evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4566–4575 (2015)
Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural. Inf. Process. Syst. 35, 24824–24837 (2022)
Welivita, A., Pu, P.: A taxonomy of empathetic response intents in human social conversations. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4886–4899 (2020)
Zhang, S., et al.: Instruction tuning for large language models: a survey. arXiv preprint arXiv:2308.10792 (2023)
Zhao, D., Han, D., Yuan, Y., Wang, C., Song, S.: Muse: a multi-scale emotional flow graph model for empathetic dialogue generation. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds.) ECML PKDD 2023. LNCS, vol. 14170, pp. 491–507. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43415-0_29
Zhao, W.X., et al.: A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)
Zhao, W., Zhao, Y., Wang, S., Qin, B.: Transesc: smoothing emotional support conversation via turn-level state transition. arXiv preprint arXiv:2305.03296 (2023)
Acknowledgments
This work was partially supported by the National Science Fund for Distinguished Young Scholars (62025205), the National Natural Science Foundation of China (No. 62032020), and the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Ethical Considerations
Lastly, we discuss the potential ethical implications of our work. The ESConv dataset is a publicly available and well-established benchmark. It has filtered the sensitive and private information, such as personally identifiable information. We aim to provide users with daily companionship and support, similar to that of family or friends, rather than professional psychological counseling or therapy.
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, H. et al. (2024). Memory-Enhanced Emotional Support Conversations with Motivation-Driven Strategy Inference. In: Bifet, A., Davis, J., Krilavičius, T., Kull, M., Ntoutsi, E., Žliobaitė, I. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14945. Springer, Cham. https://doi.org/10.1007/978-3-031-70362-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-70362-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-70361-4
Online ISBN: 978-3-031-70362-1
eBook Packages: Computer ScienceComputer Science (R0)