Cross-lingual Transfer for Automatic Question Generation by Learning Interrogative Structures in Target Languages

TL;DR

This paper introduces a cross-lingual transfer method for automatic question generation that learns interrogative structures from limited exemplars, enabling question generation in low-resource languages without requiring monolingual data.

Contribution

The proposed method effectively transfers question generation capabilities across languages using a small language model trained only on English data and minimal exemplars, without needing parallel or labeled data.

Findings

Outperforms several cross-lingual question generation baselines

Achieves performance comparable to GPT-3.5-turbo in multiple languages

Synthetic data improves multilingual question answering models

Abstract

Automatic question generation (QG) serves a wide range of purposes, such as augmenting question-answering (QA) corpora, enhancing chatbot systems, and developing educational materials. Despite its importance, most existing datasets predominantly focus on English, resulting in a considerable gap in data availability for other languages. Cross-lingual transfer for QG (XLT-QG) addresses this limitation by allowing models trained on high-resource language datasets to generate questions in low-resource languages. In this paper, we propose a simple and efficient XLT-QG method that operates without the need for monolingual, parallel, or labeled data in the target language, utilizing a small language model. Our model, trained solely on English QA datasets, learns interrogative structures from a limited set of question exemplars, which are then applied to generate questions in the target language. Experimental results show that our method outperforms several XLT-QG baselines and achieves performance comparable to GPT-3.5-turbo across different languages. Additionally, the synthetic data generated by our model proves beneficial for training multilingual QA models. With significantly fewer parameters than large language models and without requiring additional training for target languages, our approach offers an effective solution for QG and QA tasks across various languages.