@inproceedings{stowe-etal-2022-controlled,
title = "Controlled Language Generation for Language Learning Items",
author = "Stowe, Kevin and
Ghosh, Debanjan and
Zhao, Mengxuan",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.30",
doi = "10.18653/v1/2022.emnlp-industry.30",
pages = "294--305",
abstract = "This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items. We experiment with deep pretrained models for this task, developing novel methods for controlling items for factors relevant in language learning: diverse sentences for different proficiency levels and argument structure to test grammar. Human evaluation demonstrates high grammatically scores for all models (3.4 and above out of 4), and higher length (24{\%}) and complexity (9{\%}) over the baseline for the advanced proficiency model. Our results show that we can achieve strong performance while adding additional control to ensure diverse, tailored content for individual users.",
}
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%0 Conference Proceedings
%T Controlled Language Generation for Language Learning Items
%A Stowe, Kevin
%A Ghosh, Debanjan
%A Zhao, Mengxuan
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F stowe-etal-2022-controlled
%X This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items. We experiment with deep pretrained models for this task, developing novel methods for controlling items for factors relevant in language learning: diverse sentences for different proficiency levels and argument structure to test grammar. Human evaluation demonstrates high grammatically scores for all models (3.4 and above out of 4), and higher length (24%) and complexity (9%) over the baseline for the advanced proficiency model. Our results show that we can achieve strong performance while adding additional control to ensure diverse, tailored content for individual users.
%R 10.18653/v1/2022.emnlp-industry.30
%U https://aclanthology.org/2022.emnlp-industry.30
%U https://doi.org/10.18653/v1/2022.emnlp-industry.30
%P 294-305
Markdown (Informal)
[Controlled Language Generation for Language Learning Items](https://aclanthology.org/2022.emnlp-industry.30) (Stowe et al., EMNLP 2022)
ACL
- Kevin Stowe, Debanjan Ghosh, and Mengxuan Zhao. 2022. Controlled Language Generation for Language Learning Items. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 294–305, Abu Dhabi, UAE. Association for Computational Linguistics.
