# A Practical Toolkit for Multilingual Question and Answer Generation

arXiv: https://arxiv.org/abs/2305.17416 · HTML: https://mlsanity.com/paper/2305.17416/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 35 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 27 of 27)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP](https://mlsanity.com/paper/2103.00453/) (2021)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [The Effect of Natural Distribution Shift on Question Answering Models](https://mlsanity.com/paper/2004.14444/) (2020)
- [Training Question Answering Models From Synthetic Data](https://mlsanity.com/paper/2002.09599/) (2020)
- [Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation](https://mlsanity.com/paper/2104.08678/) (2021)
- [Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering](https://mlsanity.com/paper/1909.06356/) (2019)
- [Unsupervised Question Answering by Cloze Translation](https://mlsanity.com/paper/1906.04980/) (2019)
- [FQuAD: French Question Answering Dataset](https://mlsanity.com/paper/2002.06071/) (2020)
- [KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/1909.07005/) (2019)
- [GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](https://mlsanity.com/paper/2104.12741/) (2021)
- [SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis](https://mlsanity.com/paper/1912.09723/) (2019)
- [Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering](https://mlsanity.com/paper/1912.05200/) (2019)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [Varifocal Question Generation for Fact-checking](https://mlsanity.com/paper/2210.12400/) (2022)
- [SubjQA: A Dataset for Subjectivity and Review Comprehension](https://mlsanity.com/paper/2004.14283/) (2020)
- [Asking It All: Generating Contextualized Questions for any Semantic Role](https://mlsanity.com/paper/2109.04832/) (2021)
- [An Empirical Comparison of LM-based Question and Answer Generation Methods](https://mlsanity.com/paper/2305.17002/) (2023)
- [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/2202.01764/) (2022)

## Built on by (most cited first, 10 of 10)

- [Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From](https://mlsanity.com/paper/2504.10906/) (2025)
- [PACuna: Automated Fine-Tuning of Language Models for Particle Accelerators](https://mlsanity.com/paper/2310.19106/) (2023)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [HintEval: An Open-Source Python Toolkit for Hint Generation and Hint Evaluation](https://mlsanity.com/paper/2502.00857/) (2025)
- [Multi-Hop Question Generation via Dual-Perspective Keyword Guidance](https://mlsanity.com/paper/2505.15299/) (2025)
- [ISQA: Informative Factuality Feedback for Scientific Summarization](https://mlsanity.com/paper/2404.13246/) (2024)
- [Beyond Factual QA: Mentorship-Oriented Question Answering over Long-Form Multilingual Content](https://mlsanity.com/paper/2601.17173/) (2026)
- [FIQ: Fundamental Question Generation with the Integration of Question Embeddings for Video Question Answering](https://mlsanity.com/paper/2507.12816/) (2025)
- [Foundational Question Generation for Video Question Answering via an Embedding-Integrated Approach](https://mlsanity.com/paper/2511.17618/) (2025)
- [Exploiting Intrinsic Duality for Multi-Hop Question Generation](https://mlsanity.com/paper/2608.00712/) (2026)

## Abstract (arXiv)

Generating questions along with associated answers from a text has applications in several domains, such as creating reading comprehension tests for students, or improving document search by providing auxiliary questions and answers based on the query. Training models for question and answer generation (QAG) is not straightforward due to the expected structured output (i.e. a list of question and answer pairs), as it requires more than generating a single sentence. This results in a small number of publicly accessible QAG models. In this paper, we introduce AutoQG, an online service for multilingual QAG, along with lmqg, an all-in-one Python package for model fine-tuning, generation, and evaluation. We also release QAG models in eight languages fine-tuned on a few variants of pre-trained encoder-decoder language models, which can be used online via AutoQG or locally via lmqg. With these resources, practitioners of any level can benefit from a toolkit that includes a web interface for end users, and easy-to-use code for developers who require custom models or fine-grained controls for generation.