Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering

Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on Question Answering tasks. However, most of those datasets are in English, and the performances of state-of-theart 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 Crosslingual 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, establishing thus a new state-of-the-art on four multilingual datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr). * * : equal contribution. The work of Arij Riabi was partly carried out while she was working at reciTAL. 1 https://rajpurkar.github.io/ SQuAD-explorer/ et al. (2020) and Lewis et al. ( 2020a ) concurrently proposed two different evaluation sets which are comparable to the SQuAD development set. Both reach the same conclusion: due to the lack of non-English training data, models do not achieve the same performance in Non-English languages than they do in English. To the best of our knowledge, no method has been proposed to fill this gap.