Inclusive next-generation dialogue system: linguistic aspect
https://doi.org/10.18384/2949-5075-2025-1-67-82
Abstract
Aim. To identify strategies for developing language models using artificial intelligence to support the inclusion of people with mental disabilities.
Methodology. The study compares two approaches to building dialogue systems: information retrieval question-answering systems and generative question-answering. A collection of texts on inclusive education was compiled. Additionally, a complex of question-answering systems was created using neural network transfer learning methods to analyze the performance of the approaches. A linguistic analysis of the collected data and the results of the dialogue system was conducted.
Results. The study showed that both approaches to building dialogue systems have their advantages and limitations. Information retrieval question-answering systems provide high answer relevance. Generative models offer greater flexibility in a broader context. Linguistic analysis revealed that for optimal results, it is advisable to combine both approaches, leveraging the strengths of each depending on the specific task and interaction context.
Research implications. The significance lies in the development of dialogue system theory, deepening the understanding of the interaction between structural and semantic aspects of language and their impact on the effectiveness of different approaches to creating dialogue systems, as well as the possibility of applying the research results in the educational system.
About the Author
V. I. Firsanova
Saint-Petersburg State University
Russian Federation
Russian Federation
Viktorya I. Firsanova – Postgraduate Student, Department of mathematical Linguistics
Saint Petersburg
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