Abstract
Developing chatbots for the Arabic language presents unique challenges due to its complex grammar, rich vocabulary, and diverse dialects. Therefore, tailoring chatbot development methodologies and models specifically for Arabic is essential to ensure accurate understanding and generation of responses. This paper presents an empathy-driven chatbot which is a transformer-based model specifically designed for the Arabic language. The model is trained using a corpus of Arabic conversational pairs and is compared against a Seq2Seq model based on Bi-LSTM and attention mechanism. Empathy-driven chatbots aim to understand and respond to users’ emotions, needs, and concerns in a sensitive and human-like manner. By integrating empathy into chatbot design, we enhance the conversational experience, making interactions more personalized, engaging, and satisfying for users. While empathy has been extensively studied in English based chatbots, its application in other languages, such as Arabic, presents unique challenges and opportunities. Our research focuses on the development of an empathic chatbot that can understand and respond to user input in a contextually relevant and empathetic manner. The transformer based chatbot exhibits several advantages over the Seq2Seq model, including improved perplexity and BLEU score, indicating enhanced language modeling and generation capabilities.
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References
Wei, J., et al.: Leveraging large language models to power chatbots for collecting user self-reported data. arXiv preprint arXiv:2301.05843 (2023)
Liu, B., Shyam Sundar, S.: Should machines express sympathy and empathy? Experiments with a health advice chatbot. Cyberpsychol. Behav. Soc. Netw. 21(10), 625–636 (2018)
Zhou, L., et al.: The design and implementation fxiaoice, an empathetic social chatbot. Comput. Linguist. 46(1), 53–93 (2020)
Wardhana, A.K., Ferdiana, R., Hidayah, I.: Empathetic chatbot enhancement and development: a literature review. In: 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). IEEE (2021)
Khan, M.Z., Yassin, S.M.: SeerahBot:an arabic chatbot about prophet’s biography. In: International Journal of Innovative Research in Computer Science & Technology (IJIRCST) (2021)
Boussakssou, M., Ezzikouri, H., Erritali, M.: Chatbot in Arabic language using seq to seq model. Multimed. Tools Appl. 81(2), 2859–2871 (2022)
Naous, T., Hokayem, C., Hajj, H.: Empathy-driven Arabic conversational chatbot. In: Proceedings of the Fifth Arabic Natural Language Processing Workshop (2020)
Naous, T., et al.: Empathetic BERT2BERT conversational model: learning Arabic language generation with little data. arXiv preprint arXiv:2103.04353 (2021)
Abdelhay, M., Mohammed, A., Hefny, H.A.: Deeplearning for Arabic healthcare: MedicalBot. Soc. Netw. Anal. Min. 13(1), 71 (2023)
Papineni, K., et al.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (2002)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–80 (1997). https://doi.org/10.1162/neco.1997.9.8.1735. PMID: 9377276
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)
Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Lahitani, A.R., Permanasari, A.E., Setiawan, N.A.: Cosine similarity to determine similarity measure: study case in online essay assessment. In: 2016 4th International Conference on Cyber and IT Service Management, Bandung, Indonesia, pp. 1–6 (2016). https://doi.org/10.1109/CITSM.2016.7577578
Danescu-Niculescu-Mizil, C., Lee, L.: Chameleons in imagined conversations: a new approach to understanding coordination of linguistic style in dialogs. arXiv preprint arXiv:1106.3077 (2011)
Soliman, A.B., Eissa, K., El-Beltagy, S.R.: Aravec: a set of Arabic word embedding models for use in arabic NLP. Procedia Comput. Sci. 117, 256–265 (2017)
Raunak, V., Gupta, V., Metze, F.: Effective dimensionality reduction for word embeddings. In: Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) (2019)
Zhang, Y., et al.: DialoGPT: large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536 (2019)
Antoun, W., Baly, F., Hajj, H.: AraBERT: Transformer-based model for Arabic language understanding. arXiv preprint arXiv:2003.00104 (2020)
Youhadmeathello: How phrasing affects memorability Cristian Danescu-Niculescu-Mizil, Justin Cheng, Jon Kleinberg and Lillian Lee Proceedings of ACL (2012)
Mu, J., Bhat, S., Viswanath, P.: All-but-the-top: Simple and effective post-processing for word representations. arXiv preprint arXiv:1702.01417 (2017)
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Rabii, I., Boussakssou, M., Erritali, M. (2024). Empathy-Driven Chatbots for the Arabic Language: A Transformer Based Approach. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2026. Springer, Cham. https://doi.org/10.1007/978-3-031-53082-1_5
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