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Cited by 168Speech and dialogue systemsAI in Service InteractionsSocial Robot Interaction and HRI

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Conversational Agents: Goals, Technologies, Vision and Challenges

Open access
Allouch, Merav, Azaria, Amos, Azoulay, Rina·2021·Sensors
DOI 10.3390/s21248448PMC PMC8704682
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Abstract

In recent years, conversational agents (CAs) have become ubiquitous and are a presence in our daily routines. It seems that the technology has finally ripened to advance the use of CAs in various domains, including commercial, healthcare, educational, political, industrial, and personal domains. In this study, the main areas in which CAs are successful are described along with the main technologies that enable the creation of CAs. Capable of conducting ongoing communication with humans, CAs are encountered in natural-language processing, deep learning, and technologies that integrate emotional aspects. The technologies used for the evaluation of CAs and publicly available datasets are outlined. In addition, several areas for future research are identified to address moral and security issues, given the current state of CA-related technological developments. The uniqueness of our review is that an overview of the concepts and building blocks of CAs is provided, and CAs are categorized according to their abilities and main application domains. In addition, the primary tools and datasets that may be useful for the development and evaluation of CAs of different categories are described. Finally, some thoughts and directions for future research are provided, and domains that may benefit from conversational agents are introduced.

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Crossref · 10.3390/s21248448

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Figures

Figure 1 Conversational agents and chatbots: the definitions used in this article.
Figure 1 Conversational agents and chatbots: the definitions used in this article.CC BY · PubMed Central
Figure 2 Conversational-agent classification according to action capabilities.
Figure 2 Conversational-agent classification according to action capabilities.CC BY · PubMed Central
Figure 3 Conversational-agent applications.
Figure 3 Conversational-agent applications.CC BY · PubMed Central
Figure 4 The textual components of CAs.
Figure 4 The textual components of CAs.CC BY · PubMed Central
Figure 5 The main voice-based components of CAs.
Figure 5 The main voice-based components of CAs.CC BY · PubMed Central
Figure 6 The main components of a physical-based embodied CA.
Figure 6 The main components of a physical-based embodied CA.CC BY · PubMed Central
Figure 7 The main components of a goal-oriented CA.
Figure 7 The main components of a goal-oriented CA.CC BY · PubMed Central
Figure 8 Human-related aspects of the CA: emotion sensitivity, personality expression, and adaptation to the user’s taste and needs.
Figure 8 Human-related aspects of the CA: emotion sensitivity, personality expression, and adaptation to the user’s taste and needs.CC BY · PubMed Central
Figure 9 Conversational-agent applications.
Figure 9 Conversational-agent applications.CC BY · PubMed Central
Figure 10 A diagram illustrating the various CA evaluation methods.
Figure 10 A diagram illustrating the various CA evaluation methods.CC BY · PubMed Central
Figure 11 A summary of all diagrams.
Figure 11 A summary of all diagrams.CC BY · PubMed Central

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Cites

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Research works printed but not linked: 21

  1. Adiwardana, D., Luong, M.T., So, D.R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., and Lu, Y. (2020). Towards a human-like open-domain chatbot. arXiv.
  2. Nimavat, 2017, Chatbots: An overview types, architecture, tools and future possibilities, Int. J. Sci. Res. Dev., 5, 1019
  3. Gehl, 2014, Teaching to the Turing Test with Cleverbot, J. Incl. Scholarsh. Pedagog., 24, 56
  4. Noroozi, V., Zhang, Y., Bakhturina, E., and Kornuta, T. (2020). A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset. arXiv.
  5. Wallace, R.S. (2009). The anatomy of ALICE. Parsing the Turing Test, Springer Nature.
  6. Bocklisch, T., Faulkner, J., Pawlowski, N., and Nichol, A. (2017). Rasa: Open source language understanding and dialogue management. arXiv.
  7. Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv.
  8. Serban, I.V., Sankar, C., Germain, M., Zhang, S., Lin, Z., Subramanian, S., Kim, T., Pieper, M., Chandar, S., and Ke, N.R. (2017). A deep reinforcement learning chatbot. arXiv.
  9. Kim, J., Ham, D., Lee, J.G., and Kim, K.E. (2021, January 8–9). End-to-End Document-Grounded Conversation with Encoder-Decoder Pre-Trained Language Model. Proceedings of the DSTC9 Workshop, Online.
  10. Sarder, M.A. (2018). ECActive Embodied Conversational Agent for Mental Health Intervention. [Master’s Thesis, Delft University of Technology].
  11. Burgoon, J., Guerrero, L., and Manusov, V. (2011). Nonverbal signals. The SAGE Handbook of Interpersonal Communication, SAGE Publications.
  12. Liu, H., Lin, T., Sun, H., Lin, W., Chang, C.W., Zhong, T., and Rudnicky, A. (2017). Rubystar: A non-task-oriented mixture model dialog system. arXiv.
  13. Hobert, S. (2019, January 15–18). Say hello to ‘coding tutor’! design and evaluation of a chatbot-based learning system supporting students to learn to program. Proceedings of the 40th International Conference on Information Systems, ICIS 2019, Munich, Germany.
  14. Peca, A., Tapus, A., Aly, A., Pop, C., Jisa, L., Pintea, S., Rusu, A., and David, D. (2020). Exploratory study: Children’s with autism awareness of being imitated by NAO Robot. arXiv.
  15. Ross, 2018, IBM’s Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show, Stat, 25, 1
  16. Pradana, 2017, Sambot-intelligent conversational bot for interactive marketing with consumer-centric approach, Int. J. Comput. Inf. Syst. Ind. Manag. Appl., 6, 265
  17. Adams, T. (2017). AI-Powered Social Bots. arXiv.
  18. Papineni, K.A., Roukos, S., Ward, T., and Zhu, W. (2002, January 6–12). Understanding Affective Experiences with BLEU: A method for automatic evaluation of machine translation. Proceedings of the Association of Computational Linguistics, Philadelphia, PA, USA. · DOI · Paper page
  19. Guo, F., Metallinou, A., Khatri, C., Raju, A., Venkatesh, A., and Ram, A. (2018). Topic-based evaluation for conversational bots. arXiv.
  20. Danescu-Niculescu-Mizil, C., and Lee, L. (2011). Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. arXiv.
  21. Lowe, R., Pow, N., Serban, I., and Pineau, J. (2017). The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems. arXiv.

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Metadata

Authors
Allouch, M., Azaria, A., & Azoulay, R.
Affiliations
  • Ariel University (IL)
  • Jerusalem College of Technology (IL)
Corresponding author
Azoulay, Rina
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2021-12-17
Volume
21
Issue
24
Pages
8448
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s21248448
PMC
PMC8704682
OpenAlex
W4200588694
Cited by
168
References
213
Citations by year
2026: 35 · 2025: 45 · 2024: 44 · 2023: 31 · 2022: 12
FWCI
1.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
  • Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Keywords
chatbots, natural language processing, deep learning, affective computing, ethical considerations, security challenges
MeSH terms
Communication, Goals, Humans, Natural Language Processing, Technology, Vision, Ocular

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