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            <title>Proceedings of the 21st International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Karunesh</namePart>
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    <abstract>Chatbots are being widely used in educational domain to revolutionize how students interact and learn along with traditional methods of learning. This paper presents our work on LangBot, a chatbot developed for learning Tamil language. LangBot developed integrates the interactive features of chatbots with the study material of the Tamil courses offered by Tamil Virtual Academy, Government of Tamil Nadu. LangBot helps students in enhancing their learning skills and increases their interest in learning the language. Using semi-automatic methods, we generate question and answers related to all topics in the courses. We then develop a generative language model and also Retrieval Augmented Generation (RAG) so that the system can incorporate new syllabus changes. We have performed manual user studies. The results obtained are encouraging. This approach offers learners an interactive tool that aligns with their syllabus. It is observed that this enriches the overall learning experience.</abstract>
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%0 Conference Proceedings
%T LangBot-Language Learning Chatbot
%A Sundaram, Madhubala
%A RK Rao, Pattabhi
%A Lalitha Devi, Sobha
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F sundaram-etal-2024-langbot
%X Chatbots are being widely used in educational domain to revolutionize how students interact and learn along with traditional methods of learning. This paper presents our work on LangBot, a chatbot developed for learning Tamil language. LangBot developed integrates the interactive features of chatbots with the study material of the Tamil courses offered by Tamil Virtual Academy, Government of Tamil Nadu. LangBot helps students in enhancing their learning skills and increases their interest in learning the language. Using semi-automatic methods, we generate question and answers related to all topics in the courses. We then develop a generative language model and also Retrieval Augmented Generation (RAG) so that the system can incorporate new syllabus changes. We have performed manual user studies. The results obtained are encouraging. This approach offers learners an interactive tool that aligns with their syllabus. It is observed that this enriches the overall learning experience.
%U https://aclanthology.org/2024.icon-1.30/
%P 260-263

Markdown (Informal)

[LangBot-Language Learning Chatbot](https://aclanthology.org/2024.icon-1.30/) (Sundaram et al., ICON 2024)

• LangBot-Language Learning Chatbot (Sundaram et al., ICON 2024)

ACL

• Madhubala Sundaram, Pattabhi RK Rao, and Sobha Lalitha Devi. 2024. LangBot-Language Learning Chatbot. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 260–263, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).