MultiSpanQA: A Dataset for Multi-Span Question Answering
Haonan Li, Martin Tomko, Maria Vasardani, Timothy Baldwin
Abstract
Most existing reading comprehension datasets focus on single-span answers, which can be extracted as a single contiguous span from a given text passage. Multi-span questions, i.e., questions whose answer is a series of multiple discontiguous spans in the text, are common real life but are less studied. In this paper, we present MultiSpanQA, a new dataset that focuses on multi-span questions. Raw questions and contexts are extracted from the Natural Questions dataset. After multi-span re-annotation, MultiSpanQA consists of over a total of 6,000 multi-span questions in the basic version, and over 19,000 examples with unanswerable questions, and questions with single-, and multi-span answers in the expanded version. We introduce new metrics for the purposes of multi-span question answering evaluation, and establish several baselines using advanced models. Finally, we propose a new model which beats all baselines and achieves state-of-the-art on our dataset.
BibTeX
@inproceedings{li-etal-2022-multispanqa,
title = "{M}ulti{S}pan{QA}: A Dataset for Multi-Span Question Answering",
author = "Li, Haonan and
Tomko, Martin and
Vasardani, Maria and
Baldwin, Timothy",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.90/",
doi = "10.18653/v1/2022.naacl-main.90",
pages = "1250--1260"
}