On the Usability of Transformers-based models for a French Question-Answering task

• URL: http://arxiv.org/abs/2207.09150v1
• Date: Tue, 19 Jul 2022 09:46:15 GMT
• Title: On the Usability of Transformers-based models for a French Question-Answering task
• Authors: Oralie Cattan, Christophe Servan and Sophie Rosset
• Abstract summary: This paper focuses on the usability of Transformer-based language models in small-scale learning problems. We introduce a new compact model for French FrALBERT which proves to be competitive in low-resource settings.
• Score: 2.44288434255221
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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