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    <abstract>Providing example sentences that are diverse and aligned with learners’ proficiency levels is essential for fostering effective language acquisition.This study examines the use of Pre-trained Language Models (PLMs) to produce example sentences targeting L2 Japanese learners.We utilize PLMs in two ways: as quality scoring components in a retrieval system that draws from a newly curated corpus of Japanese sentences, and as direct sentence generators using zero-shot learning.We evaluate the quality of sentences by considering multiple aspects such as difficulty, diversity, and naturalness, with a panel of raters consisting of learners of Japanese, native speakers – and GPT-4.Our findings suggest that there is inherent disagreement among participants on the ratings of sentence qualities, except for difficulty. Despite that, the retrieval approach was preferred by all evaluators, especially for beginner and advanced target proficiency, while the generative approaches received lower scores on average.Even so, our experiments highlight the potential for using PLMs to enhance the adaptability of sentence suggestion systems and therefore improve the language learning journey.</abstract>
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%0 Conference Proceedings
%T Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Models
%A Benedetti, Enrico
%A Aizawa, Akiko
%A Boudin, Florian
%Y Fu, Xiyan
%Y Fleisig, Eve
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F benedetti-etal-2024-automatically
%X Providing example sentences that are diverse and aligned with learners’ proficiency levels is essential for fostering effective language acquisition.This study examines the use of Pre-trained Language Models (PLMs) to produce example sentences targeting L2 Japanese learners.We utilize PLMs in two ways: as quality scoring components in a retrieval system that draws from a newly curated corpus of Japanese sentences, and as direct sentence generators using zero-shot learning.We evaluate the quality of sentences by considering multiple aspects such as difficulty, diversity, and naturalness, with a panel of raters consisting of learners of Japanese, native speakers – and GPT-4.Our findings suggest that there is inherent disagreement among participants on the ratings of sentence qualities, except for difficulty. Despite that, the retrieval approach was preferred by all evaluators, especially for beginner and advanced target proficiency, while the generative approaches received lower scores on average.Even so, our experiments highlight the potential for using PLMs to enhance the adaptability of sentence suggestion systems and therefore improve the language learning journey.
%U https://aclanthology.org/2024.acl-srw.11
%P 114-131

Markdown (Informal)

[Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Models](https://aclanthology.org/2024.acl-srw.11) (Benedetti et al., ACL 2024)

• Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Models (Benedetti et al., ACL 2024)

ACL

• Enrico Benedetti, Akiko Aizawa, and Florian Boudin. 2024. Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 114–131, Bangkok, Thailand. Association for Computational Linguistics.