Abstract
It has been shown for English that discrete and soft prompting perform strongly in few-shot learning with pretrained language models (PLMs). In this paper, we show that discrete and soft prompting perform better than finetuning in multilingual cases: Crosslingual transfer and in-language training of multilingual natural language inference. For example, with 48 English training examples, finetuning obtains 33.74% accuracy in crosslingual transfer, barely surpassing the majority baseline (33.33%). In contrast, discrete and soft prompting outperform finetuning, achieving 36.43% and 38.79%. We also demonstrate good performance of prompting with training data in multiple languages other than English.- Anthology ID:
- 2021.emnlp-main.672
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8547–8555
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.672
- DOI:
- 10.18653/v1/2021.emnlp-main.672
- Cite (ACL):
- Mengjie Zhao and Hinrich Schütze. 2021. Discrete and Soft Prompting for Multilingual Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8547–8555, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Discrete and Soft Prompting for Multilingual Models (Zhao & Schütze, EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2021.emnlp-main.672.pdf
- Code
- mprompting/xlmrprompt
- Data
- MultiNLI