# Towards Zero-Shot Multilingual Synthetic Question and Answer Generation for Cross-Lingual Reading Comprehension

arXiv: https://arxiv.org/abs/2010.12008 · HTML: https://mlsanity.com/paper/2010.12008/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [Best Practices and Lessons Learned on Synthetic Data](https://mlsanity.com/paper/2404.07503/) (2024)
- [Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation](https://mlsanity.com/paper/2205.12647/) (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)
- [PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale](https://mlsanity.com/paper/2304.12206/) (2023)
- [Prompting-based Synthetic Data Generation for Few-Shot Question Answering](https://mlsanity.com/paper/2405.09335/) (2024)
- [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)
- [Combining Data Generation and Active Learning for Low-Resource Question Answering](https://mlsanity.com/paper/2211.14880/) (2022)
- [IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension](https://mlsanity.com/paper/2210.13778/) (2022)
- [PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development](https://mlsanity.com/paper/2301.09715/) (2023)
- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)
- [GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning](https://mlsanity.com/paper/2404.09163/) (2024)
- [ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System](https://mlsanity.com/paper/2205.14981/) (2022)
- [SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages](https://mlsanity.com/paper/2406.14425/) (2024)
- [Cross-lingual Transfer for Automatic Question Generation by Learning Interrogative Structures in Target Languages](https://mlsanity.com/paper/2410.03197/) (2024)
- [SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval](https://mlsanity.com/paper/2410.21501/) (2024)

## Abstract (arXiv)

We propose a simple method to generate multilingual question and answer pairs on a large scale through the use of a single generative model. These synthetic samples can be used to improve the zero-shot performance of multilingual QA models on target languages. Our proposed multi-task training of the generative model only requires the labeled training samples in English, thus removing the need for such samples in the target languages, making it applicable to far more languages than those with labeled data. Human evaluations indicate the majority of such samples are grammatically correct and sensible. Experimental results show our proposed approach can achieve large gains on the XQuAD dataset, reducing the gap between zero-shot and supervised performance of smaller QA models on various languages.