Rachel Keraron


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Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
Arij Riabi | Thomas Scialom | Rachel Keraron | Benoît Sagot | Djamé Seddah | Jacopo Staiano
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task. However, most of those datasets are in English, and the performances of state-of-the-art multilingual models are significantly lower when evaluated on non-English data. Due to high data collection costs, it is not realistic to obtain annotated data for each language one desires to support. We propose a method to improve the Cross-lingual Question Answering performance without requiring additional annotated data, leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion. We show that the proposed method allows to significantly outperform the baselines trained on English data only. We report a new state-of-the-art on four datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr).


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Project PIAF: Building a Native French Question-Answering Dataset
Rachel Keraron | Guillaume Lancrenon | Mathilde Bras | Frédéric Allary | Gilles Moyse | Thomas Scialom | Edmundo-Pavel Soriano-Morales | Jacopo Staiano
Proceedings of the 12th Language Resources and Evaluation Conference

Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.