@inproceedings{liu-etal-2024-optimizing,
title = "Optimizing Code-Switching in Conversational Tutoring Systems: A Pedagogical Framework and Evaluation",
author = "Liu, Zhengyuan and
Yin, Stella Xin and
Chen, Nancy",
editor = "Kawahara, Tatsuya and
Demberg, Vera and
Ultes, Stefan and
Inoue, Koji and
Mehri, Shikib and
Howcroft, David and
Komatani, Kazunori",
booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2024",
address = "Kyoto, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sigdial-1.43",
pages = "500--515",
abstract = "Large language models demonstrate remarkable proficiency in various tasks across multiple languages. However, their potential in code-switching remains underexplored, particularly in cultural and educational contexts. Code-switching or translanguaging plays a crucial role in bilingual education, facilitating comprehension and engagement among students with varied linguistic proficiencies. In this work, we present a pedagogy-inspired framework that introduces traditional classroom practices of code-switching to intelligent tutoring systems. Specifically, we develop fine-grained instructional strategies tailored to multilingual and educational needs. We conduct experiments involving both LLM-based evaluation and expert analysis to assess the effectiveness of translanguaging in tutoring dialogues. Our experimental results indicate that strategic code-switching can significantly enhance the learning experience. This work not only advances dialogic tutors in language learning, but also extends LLMs to better accommodate multilingual interaction.",
}
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<abstract>Large language models demonstrate remarkable proficiency in various tasks across multiple languages. However, their potential in code-switching remains underexplored, particularly in cultural and educational contexts. Code-switching or translanguaging plays a crucial role in bilingual education, facilitating comprehension and engagement among students with varied linguistic proficiencies. In this work, we present a pedagogy-inspired framework that introduces traditional classroom practices of code-switching to intelligent tutoring systems. Specifically, we develop fine-grained instructional strategies tailored to multilingual and educational needs. We conduct experiments involving both LLM-based evaluation and expert analysis to assess the effectiveness of translanguaging in tutoring dialogues. Our experimental results indicate that strategic code-switching can significantly enhance the learning experience. This work not only advances dialogic tutors in language learning, but also extends LLMs to better accommodate multilingual interaction.</abstract>
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%0 Conference Proceedings
%T Optimizing Code-Switching in Conversational Tutoring Systems: A Pedagogical Framework and Evaluation
%A Liu, Zhengyuan
%A Yin, Stella Xin
%A Chen, Nancy
%Y Kawahara, Tatsuya
%Y Demberg, Vera
%Y Ultes, Stefan
%Y Inoue, Koji
%Y Mehri, Shikib
%Y Howcroft, David
%Y Komatani, Kazunori
%S Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F liu-etal-2024-optimizing
%X Large language models demonstrate remarkable proficiency in various tasks across multiple languages. However, their potential in code-switching remains underexplored, particularly in cultural and educational contexts. Code-switching or translanguaging plays a crucial role in bilingual education, facilitating comprehension and engagement among students with varied linguistic proficiencies. In this work, we present a pedagogy-inspired framework that introduces traditional classroom practices of code-switching to intelligent tutoring systems. Specifically, we develop fine-grained instructional strategies tailored to multilingual and educational needs. We conduct experiments involving both LLM-based evaluation and expert analysis to assess the effectiveness of translanguaging in tutoring dialogues. Our experimental results indicate that strategic code-switching can significantly enhance the learning experience. This work not only advances dialogic tutors in language learning, but also extends LLMs to better accommodate multilingual interaction.
%U https://aclanthology.org/2024.sigdial-1.43
%P 500-515
Markdown (Informal)
[Optimizing Code-Switching in Conversational Tutoring Systems: A Pedagogical Framework and Evaluation](https://aclanthology.org/2024.sigdial-1.43) (Liu et al., SIGDIAL 2024)
ACL