Multilingual Non-Factoid Question Answering with Answer Paragraph Selection

TL;DR

This paper introduces MuNfQuAD, a large multilingual dataset for non-factoid question answering, and demonstrates that a fine-tuned Answer Paragraph Selection model achieves high accuracy and generalizes well across languages.

Contribution

The creation of MuNfQuAD, the largest multilingual non-factoid QA dataset, and the development of a fine-tuned APS model that outperforms baselines and generalizes across languages.

Findings

The dataset contains over 578K QA pairs across 38 languages.

The APS model achieved 80% accuracy on the test set.

The model effectively generalizes to unseen languages and reduces context length.

Abstract

Most existing Question Answering Datasets (QuADs) primarily focus on factoid-based short-context Question Answering (QA) in high-resource languages. However, the scope of such datasets for low-resource languages remains limited, with only a few works centered on factoid-based QuADs and none on non-factoid QuADs. Therefore, this work presents MuNfQuAD, a multilingual QuAD with non-factoid questions. It utilizes interrogative sub-headings from BBC news articles as questions and the corresponding paragraphs as silver answers. The dataset comprises over 578K QA pairs across 38 languages, encompassing several low-resource languages, and stands as the largest multilingual QA dataset to date. Based on the manual annotations of 790 QA-pairs from MuNfQuAD (golden set), we observe that 98\% of questions can be answered using their corresponding silver answer. Our fine-tuned Answer Paragraph Selection (APS) model outperforms the baselines. The APS model attained an accuracy of 80\% and 72\%, as well as a macro F1 of 72\% and 66\%, on the MuNfQuAD testset and the golden set, respectively. Furthermore, the APS model effectively generalizes a certain language within the golden set, even after being fine-tuned on silver labels. We also observe that the fine-tuned APS model is beneficial for reducing the context of a question. These findings suggest that this resource would be a valuable contribution to the QA research community.