Abstract
This study evaluates the effectiveness of pre-trained language models in identifying argument structure constructions, important for modeling both first and second language learning. We examine three methodologies: (1) supervised training with RoBERTa using a gold-standard ASC treebank, including by-tag accuracy evaluation for sentences from both native and non-native English speakers, (2) prompt-guided annotation with GPT-4, and (3) generating training data through prompts with GPT-4, followed by RoBERTa training. Our findings indicate that RoBERTa trained on gold-standard data shows the best performance. While data generated through GPT-4 enhances training, it does not exceed the benchmarks set by gold-standard data.- Anthology ID:
- 2024.emnlp-main.415
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7302–7314
- Language:
- URL:
- https://aclanthology.org/2024.emnlp-main.415
- DOI:
- 10.18653/v1/2024.emnlp-main.415
- Cite (ACL):
- Hakyung Sung and Kristopher Kyle. 2024. Leveraging pre-trained language models for linguistic analysis: A case of argument structure constructions. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7302–7314, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging pre-trained language models for linguistic analysis: A case of argument structure constructions (Sung & Kyle, EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.emnlp-main.415.pdf