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

arXiv: https://arxiv.org/abs/2010.12643 · HTML: https://mlsanity.com/paper/2010.12643/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 30 of 41)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Fine-tuned Language Models are Continual Learners](https://mlsanity.com/paper/2205.12393/) (2022)
- [Best Practices and Lessons Learned on Synthetic Data](https://mlsanity.com/paper/2404.07503/) (2024)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [Generating Scientific Claims for Zero-Shot Scientific Fact Checking](https://mlsanity.com/paper/2203.12990/) (2022)
- [One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval](https://mlsanity.com/paper/2107.11976/) (2021)
- [QAmeleon: Multilingual QA with Only 5 Examples](https://mlsanity.com/paper/2211.08264/) (2022)
- [DataGen: Unified Synthetic Dataset Generation via Large Language Models](https://mlsanity.com/paper/2406.18966/) (2024)
- [IT5: Text-to-text Pretraining for Italian Language Understanding and Generation](https://mlsanity.com/paper/2203.03759/) (2022)
- [Camoscio: an Italian Instruction-tuned LLaMA](https://mlsanity.com/paper/2307.16456/) (2023)
- [PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale](https://mlsanity.com/paper/2304.12206/) (2023)
- [Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges](https://mlsanity.com/paper/2309.12426/) (2023)
- [CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale](https://mlsanity.com/paper/2502.16645/) (2025)
- [AskQE: Question Answering as Automatic Evaluation for Machine Translation](https://mlsanity.com/paper/2504.11582/) (2025)
- [Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation](https://mlsanity.com/paper/2110.07150/) (2021)
- [The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer](https://mlsanity.com/paper/2204.06457/) (2022)
- [IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension](https://mlsanity.com/paper/2210.13778/) (2022)
- [CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search](https://mlsanity.com/paper/2305.11626/) (2023)
- [Synthetic Interaction Data for Scalable Personalization in Large Language Models](https://mlsanity.com/paper/2602.12394/) (2026)
- [MaskEval: Weighted MLM-Based Evaluation for Text Summarization and Simplification](https://mlsanity.com/paper/2205.12394/) (2022)
- [GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning](https://mlsanity.com/paper/2404.09163/) (2024)
- [ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions](https://mlsanity.com/paper/2509.16543/) (2025)
- [Interpreting Indirect Answers to Yes-No Questions in Multiple Languages](https://mlsanity.com/paper/2310.13290/) (2023)
- [QUARTZ : QA-based Unsupervised Abstractive Refinement for Task-oriented Dialogue Summarization](https://mlsanity.com/paper/2509.26302/) (2025)
- [ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System](https://mlsanity.com/paper/2205.14981/) (2022)
- [Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model](https://mlsanity.com/paper/2207.06839/) (2022)
- [Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension](https://mlsanity.com/paper/2302.13241/) (2023)
- [Promoting Generalized Cross-lingual Question Answering in Few-resource Scenarios via Self-knowledge Distillation](https://mlsanity.com/paper/2309.17134/) (2023)
- [Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptation](https://mlsanity.com/paper/2404.14339/) (2024)
- [QOG:Question and Options Generation based on Language Model](https://mlsanity.com/paper/2406.12381/) (2024)

## Abstract (arXiv)

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).