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.
Anthology ID:
2022.naacl-main.90
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1250–1260
Language:
URL:
https://aclanthology.org/2022.naacl-main.90
DOI:
10.18653/v1/2022.naacl-main.90
Bibkey:
Cite (ACL):
Haonan Li, Martin Tomko, Maria Vasardani, and Timothy Baldwin. 2022. MultiSpanQA: A Dataset for Multi-Span Question Answering. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1250–1260, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
MultiSpanQA: A Dataset for Multi-Span Question Answering (Li et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.90.pdf
Code
 haonan-li/MultiSpanQA
Data
BookTestCoQADROPELI5HotpotQAMS MARCONatural QuestionsQuACQuorefSQuADSearchQAWikiQA