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AuthorHamad, Omama
AuthorHamdi, Ali
AuthorShaban, Khaled
Available date2022-12-21T10:01:46Z
Publication Date2022
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
URIhttp://dx.doi.org/10.1007/978-3-031-16270-1_43
URIhttp://hdl.handle.net/10576/37504
AbstractThere 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. 2022, Springer Nature Switzerland AG.
SponsorAcknowledgments. 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.
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectChatbots
Deep learning
Empathetic dialogue
Natural language generation
TitleEmpathy and Persona of English vs. Arabic Chatbots: A Survey and Future Directions
TypeConference Paper
Pagination525-537
Volume Number13502 LNAI
dc.accessType Abstract Only


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