Grammar Control in Dialogue Response Generation for Language Learning Chatbots
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Abstract (arXiv)
Chatbots based on large language models offer cheap conversation practice opportunities for language learners. However, they are hard to control for linguistic forms that correspond to learners' current needs, such as grammar. We control grammar in chatbot conversation practice by grounding a dialogue response generation model in a pedagogical repository of grammar skills. We also explore how this control helps learners to produce specific grammar. We comprehensively evaluate prompting, fine-tuning, and decoding strategies for grammar-controlled dialogue response generation. Strategically decoding Llama3 outperforms GPT-3.5 when tolerating minor response quality losses. Our simulation predicts grammar-controlled responses to support grammar acquisition adapted to learner proficiency. Existing language learning chatbots and research on second language acquisition benefit from these affordances. Code available on GitHub.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 34 references that are in the map. First posted on arXiv 11 Feb 2025.
Builds on
12 of 34 references.
References · 12
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Citing papers per year: 2026: 2.
2 papers.