On the Usability of Transformers-based Models for a French Question-Answering Task

Oralie Cattan, Christophe Servan, Sophie Rosset


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
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
244–255
Language:
URL:
https://aclanthology.org/2021.ranlp-1.29
DOI:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.ranlp-1.29.pdf
Data
CCNetFQuADFrench WikipediaSQuAD