Papers by Rachel Keraron
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering (2021.emnlp-main)
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| Challenge: | Existing methods to improve Question Answering performance on non-English data are expensive and limited to evaluation set. |
| Approach: | They propose a method to improve Question Answering performance without additional annotations by leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion. |
| Outcome: | The proposed method outperforms baselines on four datasets in English significantly . the proposed model outperformed baselines in english and is comparable to the validation set of the original SQuAD. |
Project PIAF: Building a Native French Question-Answering Dataset (2020.lrec-1)
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Rachel Keraron, Guillaume Lancrenon, Mathilde Bras, Frédéric Allary, Gilles Moyse, Thomas Scialom, Edmundo-Pavel Soriano-Morales, Jacopo Staiano
| Challenge: | a lack of data for non-English languages is limiting the development of downstream tasks such as Question Answering. |
| Approach: | They propose to collect a native French Question Answering Dataset using a participatory setup. |
| Outcome: | The proposed tool allows volunteers to participate in crowdsourced annotations in French. |