Abstract
This paper proposes a new second language learning task of generating a response including specified grammatical items. We consider two approaches: 1) fine-tuning a pre-trained language model (DialoGPT) by reinforcement learning and 2) providing a few-shot prompt to a large language model (GPT-3). For reinforcement learning, we examine combinations of three reward functions that consider grammatical items, diversity, and fluency. Our experiments confirm that both approaches can generate responses including the specified grammatical items and that it is crucial to consider fluency rather than diversity as the reward function.- Anthology ID:
- 2023.bea-1.16
- Volume:
- Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 184–194
- Language:
- URL:
- https://aclanthology.org/2023.bea-1.16
- DOI:
- 10.18653/v1/2023.bea-1.16
- Cite (ACL):
- Yuki Okano, Kotaro Funakoshi, Ryo Nagata, and Manabu Okumura. 2023. Generating Dialog Responses with Specified Grammatical Items for Second Language Learning. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 184–194, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Generating Dialog Responses with Specified Grammatical Items for Second Language Learning (Okano et al., BEA 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.bea-1.16.pdf