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
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.naacl-main.90.pdf
- Code
- haonan-li/MultiSpanQA
- Data
- BookTest, CoQA, DROP, ELI5, HotpotQA, MS MARCO, Natural Questions, QuAC, Quoref, SQuAD, SearchQA, WikiQA