Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
Arij Riabi, Thomas Scialom, Rachel Keraron, Benoît Sagot, Djamé Seddah, Jacopo Staiano
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 datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr).- Anthology ID:
- 2021.emnlp-main.562
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7016–7030
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.562
- DOI:
- 10.18653/v1/2021.emnlp-main.562
- Cite (ACL):
- Arij Riabi, Thomas Scialom, Rachel Keraron, Benoît Sagot, Djamé Seddah, and Jacopo Staiano. 2021. Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7016–7030, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering (Riabi et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2021.emnlp-main.562.pdf
- Code
- microsoft/unilm
- Data
- MLQA, SQuAD, XQuAD