A Systematic Review of Natural Language Understanding - Related Challenges in Conversational Agent Developmentspan>
A Systematic Review of Natural Language Understanding - Related Challenges in Conversational Agent Development |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-6 |
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| Year of Publication : 2025 | ||
| Author : Godson Chetachi Uzoaru, Ikechukwu Ignatius Ayogu, Juliet Nnenna Odii, Aloysius Chijioke Onyeka, Matthew Emeka Nwanga, Francisca Onyinyechi Nwokoma, Euphemia Chioma Nwokorie, Obi Chukwuemeka Nwokonkwo, Chinwe Gilean Onukwugha1 and Anthony Ifeanyi Otuonye | ||
| DOI : 10.14445/22315381/IJETT-V73I6P143 | ||
How to Cite?
Godson Chetachi Uzoaru, Ikechukwu Ignatius Ayogu, Juliet Nnenna Odii, Aloysius Chijioke Onyeka, Matthew Emeka Nwanga, Francisca Onyinyechi Nwokoma, Euphemia Chioma Nwokorie, Obi Chukwuemeka Nwokonkwo, Chinwe Gilean Onukwugha1 and Anthony Ifeanyi Otuonye , "A Systematic Review of Natural Language Understanding - Related Challenges in Conversational Agent Development," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.520-549, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P143
Abstract
Conversational agents have become an integral part of modern digital interactions. These artificial intelligence agents rapidly transform how organizations, businesses, and individuals communicate. These agents provide a diverse range of operations, including customer service, automation of regular processes, improvement of user engagement, and delivery of personalized experiences. However, the potential of chatbots in new application scenarios is hindered in many perspectives. This review analyzes recent trends in the development and application of chatbots to generate a deeper understating of the natural language understanding-related challenges that currently impede the extension or application of the state-of-the-art to low-resource languages. The motivation is to fill the gap between the highly resourced and the low-resource languages, particularly those of Nigeria and Africa. Based on the analysis of extant literature, this paper notes that the most challenging issues are related to the inability to disambiguate linguistic context accurately by the existing conversational agent technology. Accordingly, conversational agents encounter certain noteworthy obstacles in natural language understanding that negatively affect their capacity to communicate efficiently and in a natural way with humans due to difficulties linked to contextual ambiguity, limitations associated with managing inputs in diverse languages, deficit in knowledge required to accurately recognize sentiment and intent, lack of capacity for commonsense reasoning and pragmatics. It is, therefore, imperative to harness the strengths of the state-of-the-art methodologies or approaches into a framework that is easily transferable to low-resource language scenarios to speedily extend the reach of the very important digital conversational assistants to the underserved populations in Nigeria and the majority of Africa.
Keywords
Conversational agents, Natural language processing, Context modeling, Contextual ambiguity, Distributional semantics.
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