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DC Field | Value | Language |
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dc.contributor.author | Maleesha, P.L.A.S. | - |
dc.date.accessioned | 2025-07-04T05:29:03Z | - |
dc.date.available | 2025-07-04T05:29:03Z | - |
dc.date.issued | 2024-09-19 | - |
dc.identifier.uri | https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4821 | - |
dc.description.abstract | ABSTRACT The language inclusion of banking services is still a crucial concern in an era where digital banking is becoming more and more common, especially for people who do not understand English. In order to bridge the linguistic gap in Sri Lanka's banking industry, this thesis presents a novel banking chatbot system in the Sinhala language. Through the utilization of OpenAI's Large Language Models, cutting-edge translation APIs, and the Rasa framework, this research leads the way in creating a chatbot that can comprehend and reply to user inquiries in Sinhala, thereby greatly improving the accessibility of banking services for Sinhala speakers. The comprehensive integration of natural language processing techniques used in this research's approach entails translating Sinhala questions into English, processing those queries to provide pertinent responses, and then translating those responses back into Sinhala. This method preserves the linguistic and cultural subtleties of the user's original query while simultaneously guaranteeing the correctness and applicability of the chatbot's responses. The architecture of the chatbot is made to be reliable, scalable, and able to handle a variety of banking-related queries, such as those for transactional services or account information. Keywords: Sinhala Language, Digital Banking, Chatbot Technology, Rasa Framework, OpenAI, Large Language Models, Natural Language Processing, Translation API, User Experience, Linguistic Inclusivity. | en_US |
dc.language.iso | en | en_US |
dc.title | Design and Development of a Sinhala Banking Chatbot System | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | 2023 |
Files in This Item:
File | Description | Size | Format | |
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2020MCS053.pdf | 1.9 MB | Adobe PDF | View/Open |
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