# Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering Source: [Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:39:16.015Z Authors: Arij Riabi, Thomas Scialom, Rachel Keraron, Beno\\^it Sagot, Djam\\'e Seddah, Jacopo Staiano Published: 2020-10-23T20:09:01.000Z ## Abstract 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 multilingual datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()