# Cross-Lingual Training for Automatic Question Generation

arXiv: https://arxiv.org/abs/1906.02525 · HTML: https://mlsanity.com/paper/1906.02525/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 23 of 23)

- [Best Practices and Lessons Learned on Synthetic Data](https://mlsanity.com/paper/2404.07503/) (2024)
- [QAmeleon: Multilingual QA with Only 5 Examples](https://mlsanity.com/paper/2211.08264/) (2022)
- [ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation](https://mlsanity.com/paper/2106.01597/) (2021)
- [A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT](https://mlsanity.com/paper/2401.07098/) (2024)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [Meta-Learning for Effective Multi-task and Multilingual Modelling](https://mlsanity.com/paper/2101.10368/) (2021)
- [Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications](https://mlsanity.com/paper/2405.11579/) (2024)
- [PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale](https://mlsanity.com/paper/2304.12206/) (2023)
- [Diversify Question Generation with Retrieval-Augmented Style Transfer](https://mlsanity.com/paper/2310.14503/) (2023)
- [Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering](https://mlsanity.com/paper/2504.07583/) (2025)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation](https://mlsanity.com/paper/2203.10250/) (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)
- [Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies](https://mlsanity.com/paper/2103.10121/) (2021)
- [ECIS-VQG: Generation of Entity-centric Information-seeking Questions from Videos](https://mlsanity.com/paper/2410.09776/) (2024)
- [WARM: A Weakly (+Semi) Supervised Model for Solving Math word Problems](https://mlsanity.com/paper/2104.06722/) (2021)
- [ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System](https://mlsanity.com/paper/2205.14981/) (2022)
- [Cross-lingual Transfer for Automatic Question Generation by Learning Interrogative Structures in Target Languages](https://mlsanity.com/paper/2410.03197/) (2024)
- [Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesis](https://mlsanity.com/paper/2508.10013/) (2025)
- [GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children](https://mlsanity.com/paper/2305.16809/) (2023)
- [Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems](https://mlsanity.com/paper/2406.06127/) (2024)

## Abstract (arXiv)

Automatic question generation (QG) is a challenging problem in natural language understanding. QG systems are typically built assuming access to a large number of training instances where each instance is a question and its corresponding answer. For a new language, such training instances are hard to obtain making the QG problem even more challenging. Using this as our motivation, we study the reuse of an available large QG dataset in a secondary language (e.g. English) to learn a QG model for a primary language (e.g. Hindi) of interest. For the primary language, we assume access to a large amount of monolingual text but only a small QG dataset. We propose a cross-lingual QG model which uses the following training regime: (i) Unsupervised pretraining of language models in both primary and secondary languages and (ii) joint supervised training for QG in both languages. We demonstrate the efficacy of our proposed approach using two different primary languages, Hindi and Chinese. We also create and release a new question answering dataset for Hindi consisting of 6555 sentences.