Abstract
For many tasks, state-of-the-art results have been achieved with Transformer-based architectures, resulting in a paradigmatic shift in practices from the use of task-specific architectures to the fine-tuning of pre-trained language models. The ongoing trend consists in training models with an ever-increasing amount of data and parameters, which requires considerable resources. It leads to a strong search to improve resource efficiency based on algorithmic and hardware improvements evaluated only for English. This raises questions about their usability when applied to small-scale learning problems, for which a limited amount of training data is available, especially for under-resourced languages tasks. The lack of appropriately sized corpora is a hindrance to applying data-driven and transfer learning-based approaches with strong instability cases. In this paper, we establish a state-of-the-art of the efforts dedicated to the usability of Transformer-based models and propose to evaluate these improvements on the question-answering performances of French language which have few resources. We address the instability relating to data scarcity by investigating various training strategies with data augmentation, hyperparameters optimization and cross-lingual transfer. We also introduce a new compact model for French FrALBERT which proves to be competitive in low-resource settings.- Anthology ID:
- 2021.ranlp-1.29
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 244–255
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.29
- DOI:
- Cite (ACL):
- Oralie Cattan, Christophe Servan, and Sophie Rosset. 2021. On the Usability of Transformers-based Models for a French Question-Answering Task. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 244–255, Held Online. INCOMA Ltd..
- Cite (Informal):
- On the Usability of Transformers-based Models for a French Question-Answering Task (Cattan et al., RANLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.ranlp-1.29.pdf
- Data
- CCNet, FQuAD, French Wikipedia, SQuAD