Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
This addresses the data scarcity issue for cross-lingual question answering, enabling better support for multiple languages without costly annotation, though it is incremental as it builds on existing multilingual models and data augmentation techniques.
The paper tackles the problem of low performance of multilingual question answering models on non-English data due to lack of annotated datasets, by proposing a synthetic data augmentation method using question generation models, which achieves new state-of-the-art results on four multilingual datasets.
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).