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        <title>LLM-based Code-Switched Text Generation for Grammatical Error Correction</title>
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            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
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            <namePart type="given">Mohit</namePart>
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    <abstract>With the rise of globalisation, code-switching (CSW) has become a ubiquitous part of multilingual conversation, posing new challenges for natural language processing (NLP), especially in Grammatical Error Correction (GEC). This work explores the complexities of applying GEC systems to CSW texts. Our objectives include evaluating the performance of state-of-the-art GEC systems on an authentic CSW dataset from English as a Second Language (ESL) learners, exploring synthetic data generation as a solution to data scarcity, and developing a model capable of correcting grammatical errors in monolingual and CSW texts. We generated synthetic CSW GEC data, resulting in one of the first substantial datasets for this task, and showed that a model trained on this data is capable of significant improvements over existing systems. This work targets ESL learners, aiming to provide educational technologies that aid in the development of their English grammatical correctness without constraining their natural multilingualism.</abstract>
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%0 Conference Proceedings
%T LLM-based Code-Switched Text Generation for Grammatical Error Correction
%A Potter, Tom
%A Yuan, Zheng
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F potter-yuan-2024-llm
%X With the rise of globalisation, code-switching (CSW) has become a ubiquitous part of multilingual conversation, posing new challenges for natural language processing (NLP), especially in Grammatical Error Correction (GEC). This work explores the complexities of applying GEC systems to CSW texts. Our objectives include evaluating the performance of state-of-the-art GEC systems on an authentic CSW dataset from English as a Second Language (ESL) learners, exploring synthetic data generation as a solution to data scarcity, and developing a model capable of correcting grammatical errors in monolingual and CSW texts. We generated synthetic CSW GEC data, resulting in one of the first substantial datasets for this task, and showed that a model trained on this data is capable of significant improvements over existing systems. This work targets ESL learners, aiming to provide educational technologies that aid in the development of their English grammatical correctness without constraining their natural multilingualism.
%U https://aclanthology.org/2024.emnlp-main.942
%P 16957-16965

Markdown (Informal)

[LLM-based Code-Switched Text Generation for Grammatical Error Correction](https://aclanthology.org/2024.emnlp-main.942) (Potter & Yuan, EMNLP 2024)

• LLM-based Code-Switched Text Generation for Grammatical Error Correction (Potter & Yuan, EMNLP 2024)

ACL

• Tom Potter and Zheng Yuan. 2024. LLM-based Code-Switched Text Generation for Grammatical Error Correction. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 16957–16965, Miami, Florida, USA. Association for Computational Linguistics.