Abstract
There is a high demand for chatbots across a wide range of sectors. Human-like chatbots engage meaningfully in dialogues while interpreting and expressing emotions and being consistent through understanding the user’s personality. Though substantial progress has been achieved in developing empathetic chatbots for English, work on Arabic chatbots is still in its early stages due to various challenges associated with the language constructs and dialects. This survey reviews recent literature on approaches to empathetic response generation, persona modelling and datasets for developing chatbots in the English language. In addition, it presents the challenges of applying these approaches to Arabic and outlines some solutions. We focus on open-domain chatbots developed as end-to-end generative systems due to their capabilities to learn and infer language and emotions. Accordingly, we create four open problems pertaining to gaps in Arabic and English work; namely, (1) feature representation learning based on multiple dialects; (2) modelling the various facets of a persona and emotions; (3) datasets; and (4) evaluation metrics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdelali, A., Darwish, K., Durrani, N., Mubarak, H.: Farasa: a fast and furious segmenter for Arabic. In: Proceedings of the 2016 conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 11–16 (2016)
Abu Ali, D., Habash, N.: Botta: an Arabic dialect chatbot. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pp. 208–212. The COLING 2016 Organizing Committee, Osaka, Japan, December 2016
AlHumoud, S., Al Wazrah, A., Aldamegh, W.: Arabic chatbots: a survey. Int. J. Adv. Comput. Sci. Appl. 535–541 (2018)
Aliwy, A., Taher, H., AboAltaheen, Z.: Arabic dialects identification for all Arabic countries. In: Proceedings of the Fifth Arabic Natural Language Processing Workshop, pp. 302–307. Association for Computational Linguistics, Barcelona, Spain (Online), December 2020
Almiman, A., Osman, N., Torki, M.: Deep neural network approach for Arabic community question answering. Alex. Eng. J. 59(6), 4427–4434 (2020)
Antoun, W., Baly, F., Hajj, H.: AraBERT: transformer-based model for Arabic language understanding. In: LREC 2020 Workshop Language Resources and Evaluation Conference, 11–16 May 2020, p. 9 (2020)
Beredo, J., Bautista, C.M., Cordel, M., Ong, E.: Generating empathetic responses with a pre-trained conversational model. In: Ekštein, K., Pártl, F., Konopík, M. (eds.) TSD 2021. LNCS (LNAI), vol. 12848, pp. 147–158. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-83527-9_13
Caldarini, G., Jaf, S., McGarry, K.: A literature survey of recent advances in chatbots. Information 13(1), 41 (2022)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, June 2019
Dinan, E., et al.: The second conversational intelligence challenge (ConvAI2). In: Escalera, S., Herbrich, R. (eds.) The NeurIPS ’18 Competition. TSSCML, pp. 187–208. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29135-8_7
Firdaus, M., Jain, U., Ekbal, A., Bhattacharyya, P.: SEPRG: sentiment aware emotion controlled personalized response generation. In: Proceedings of the 14th International Conference on Natural Language Generation, pp. 353–363. Association for Computational Linguistics, Aberdeen, August 2021
Firdaus, M., Thangavelu, N., Ekba, A., Bhattacharyya, P.: Persona aware response generation with emotions. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2020)
Fu, T., Gao, S., Zhao, X., Wen, J.R., Yan, R.: Learning towards conversational AI: a survey. AI Open (2022)
Guilera, T., Batalla, I., Forné, C., Soler-González, J.: Empathy and big five personality model in medical students and its relationship to gender and specialty preference: a cross-sectional study. BMC Med. Educ. 19(1), 1–8 (2019)
Huang, M., Zhu, X., Gao, J.: Challenges in building intelligent open-domain dialog systems. ACM Trans. Inf. Syst. (TOIS) 38(3), 1–32 (2020)
Kusner, M.J., Hernández-Lobato, J.M.: GANs for sequences of discrete elements with the Gumbel-softmax distribution. arXiv preprint arXiv:1611.04051 (2016)
Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004)
Lin, Z., Madotto, A., Shin, J., Xu, P., Fung, P.: Moel: mixture of empathetic listeners. arXiv preprint arXiv:1908.07687 (2019)
Lin, Z., et al.: Caire: an end-to-end empathetic chatbot. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13622–13623 (2020)
Liu, Q., et al.: You impress me: dialogue generation via mutual persona perception. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1417–1427. Association for Computational Linguistics, Online, July 2020
Liu, Y., Maier, W., Minker, W., Ultes, S.: Empathetic dialogue generation with pre-trained RobERTa-GPT2 and external knowledge. arXiv preprint arXiv:2109.03004 (2021)
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Lowe, R., Noseworthy, M., Serban, I.V., Angelard-Gontier, N., Bengio, Y., Pineau, J.: Towards an automatic turing test: learning to evaluate dialogue responses. arXiv preprint arXiv:1708.07149 (2017)
Lubis, N., Sakti, S., Yoshino, K., Nakamura, S.: Eliciting positive emotion through affect-sensitive dialogue response generation: a neural network approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Majumder, N., et al.: Mime: mimicking emotions for empathetic response generation. arXiv preprint arXiv:2010.01454 (2020)
Miller, T., Pedell, S., Lopez-Lorca, A.A., Mendoza, A., Sterling, L., Keirnan, A.: Emotion-led modelling for people-oriented requirements engineering: the case study of emergency systems. J. Syst. Softw. 105, 54–71 (2015)
Naous, T., Antoun, W., Mahmoud, R., Hajj, H.: Empathetic BERT2BERT conversational model: learning Arabic language generation with little data. In: Proceedings of the Sixth Arabic Natural Language Processing Workshop, pp. 164–172. Association for Computational Linguistics, Kyiv, Ukraine (Virtual), April 2021
Naous, T., Hokayem, C., Hajj, H.: Empathy-driven Arabic conversational chatbot. In: Proceedings of the Fifth Arabic Natural Language Processing Workshop, pp. 58–68. Association for Computational Linguistics, Barcelona, Spain (Online), December 2020
Neme, A.A., Paumier, S.: Restoring Arabic vowels through omission-tolerant dictionary lookup. Lang. Resour. Eval. 54(2), 487–551 (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)
Plutchik, R., Kellerman, H.: Emotion, Theory, Research, and Experience. Academic Press, Cambridge (1980)
Rashkin, H., Smith, E.M., Li, M., Boureau, Y.L.: Towards empathetic open-domain conversation models: a new benchmark and dataset. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5370–5381. Association for Computational Linguistics, Florence, Italy, July 2019
Reniers, R.L., Corcoran, R., Drake, R., Shryane, N.M., Völlm, B.A.: The QCAE: a questionnaire of cognitive and affective empathy. J. Pers. Assess. 93(1), 84–95 (2011)
Roller, S., et al.: Recipes for building an open-domain chatbot. arXiv preprint arXiv:2004.13637 (2020)
de Rosa, G.H., Papa, J.P.: A survey on text generation using generative adversarial networks. Pattern Recogn. 119, 108098 (2021)
Salminen, J., Rao, R.G., Jung, S., Chowdhury, S.A., Jansen, B.J.: Enriching social media personas with personality traits: a deep learning approach using the big five classes. In: Degen, H., Reinerman-Jones, L. (eds.) HCII 2020. LNCS, vol. 12217, pp. 101–120. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50334-5_7
Sharma, A., Lin, I.W., Miner, A.S., Atkins, D.C., Althoff, T.: Towards facilitating empathic conversations in online mental health support: a reinforcement learning approach. In: Proceedings of the Web Conference 2021, pp. 194–205 (2021)
Song, H., Wang, Y., Zhang, K., Zhang, W.N., Liu, T.: BoB: BERT over BERT for training persona-based dialogue models from limited personalized data. 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. 167–177. Association for Computational Linguistics, Online, August 2021
Su, H., Jhan, J.H., Sun, F.Y., Sahay, S., Lee, H.Y.: Put chatbot into its interlocutor’s shoes: new framework to learn chatbot responding with intention. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1559–1569. Association for Computational Linguistics, Online, June 2021
Tang, F., Zeng, L., Wang, F., Zhou, J.: Persona authentication through generative dialogue. arXiv preprint arXiv:2110.12949 (2021)
Toussaint, L., Webb, J.R.: Gender differences in the relationship between empathy and forgiveness. J. Soc. Psychol. 145(6), 673–685 (2005)
Yang, D., Flek, L.: Towards user-centric text-to-text generation: a survey. In: Ekštein, K., Pártl, F., Konopík, M. (eds.) TSD 2021. LNCS (LNAI), vol. 12848, pp. 3–22. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-83527-9_1
Zaranis, E., Paraskevopoulos, G., Katsamanis, A., Potamianos, A.: EmpBot: a t5-based empathetic chatbot focusing on sentiments. arXiv preprint arXiv:2111.00310 (2021)
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., Weston, J.: Personalizing dialogue agents: I have a dog, do you have pets too? In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2204–2213. Association for Computational Linguistics, Melbourne, Australia, July 2018
Zhong, P., Zhang, C., Wang, H., Liu, Y., Miao, C.: Towards persona-based empathetic conversational models. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6556–6566. Association for Computational Linguistics, Online, November 2020
Acknowledgments
This work was made possible by NPRP13S-0112-200037 grant from Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Hamad, O., Hamdi, A., Shaban, K. (2022). Empathy and Persona of English vs. Arabic Chatbots: A Survey and Future Directions. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2022. Lecture Notes in Computer Science(), vol 13502. Springer, Cham. https://doi.org/10.1007/978-3-031-16270-1_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-16270-1_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16269-5
Online ISBN: 978-3-031-16270-1
eBook Packages: Computer ScienceComputer Science (R0)