ABSTRACT
Machine reading comprehension (MRC) is an essential task for many question-answering applications. However, existing MRC datasets mainly focus on data with single answer and overlook multiple answers, which are common in the real world. In this paper, we aim to construct an MRC dataset with both data of single answer and multiple answers. To achieve this purpose, we design a novel pipeline method: data collection, data cleaning, question generation and test set annotation. Based on these procedures, we construct a high-quality multi-answer MRC dataset (MA-MRC) with 129K question-answer-context samples. We implement a sequence of baselines and carry out extensive experiments on MA-MRC. According to the experimental results, MA-MRC is a challenging dataset, which can facilitate the future research on the multi-answer MRC task.
Supplemental Material
- Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007. Dbpedia: A nucleus for a web of open data. In The semantic web. Springer, 722--735.Google ScholarDigital Library
- Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al. 2016. Ms marco: A human generated machine reading comprehension dataset. arXiv preprint arXiv:1611.09268 (2016).Google Scholar
- Antoine Bordes, Nicolas Usunier, Sumit Chopra, and Jason Weston. 2015. Largescale simple question answering with memory networks. arXiv preprint arXiv:1506.02075 (2015).Google Scholar
- Pere-Lluís Huguet Cabot and Roberto Navigli. 2021. REBEL: Relation extraction by end-to-end language generation. In Findings of the Association for Computational Linguistics: EMNLP 2021. 2370--2381.Google ScholarCross Ref
- Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Édouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised Cross-lingual Representation Learning at Scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 8440--8451.Google ScholarCross Ref
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 4171--4186.Google Scholar
- Matthew Dunn, Levent Sagun, Mike Higgins, V Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017. Searchqa: A new q&a dataset augmented with context from a search engine. arXiv preprint arXiv:1704.05179 (2017).Google Scholar
- Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yuan Liu, Yizhong Wang, HuaWu, Qiaoqiao She, et al. 2018. DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications. In Proceedings of the Workshop on Machine Reading for Question Answering. 37--46.Google ScholarCross Ref
- Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. Advances in neural information processing systems 28 (2015).Google Scholar
- Felix Hill, Antoine Bordes, Sumit Chopra, and JasonWeston. 2015. The goldilocks principle: Reading children's books with explicit memory representations. arXiv preprint arXiv:1511.02301 (2015).Google Scholar
- Mandar Joshi, Eunsol Choi, Daniel SWeld, and Luke Zettlemoyer. 2017. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1601--1611.Google ScholarCross Ref
- Daniel Khashabi, Yeganeh Kordi, and Hannaneh Hajishirzi. 2022. Unifiedqav2: Stronger generalization via broader cross-format training. arXiv preprint arXiv:2202.12359 (2022).Google Scholar
- Tomá? Kocisky, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018. The NarrativeQA Reading Comprehension Challenge. Transactions of the Association for Computational Linguistics 6 (2018), 317--328.Google ScholarCross Ref
- Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017. RACE: Large-scale ReAding Comprehension Dataset From Examinations. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 785--794.Google ScholarCross Ref
- Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 7871--7880.Google ScholarCross Ref
- Haonan Li, Martin Tomko, Maria Vasardani, and Timothy Baldwin. 2022. Multi-SpanQA: 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. 1250--1260.Google Scholar
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).Google Scholar
- Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 1003--1011.Google ScholarDigital Library
- Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140 (2020), 1--67.Google Scholar
- Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000 Questions for Machine Comprehension of Text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2383--2392.Google ScholarCross Ref
- Matthew Richardson, Christopher JC Burges, and Erin Renshaw. 2013. Mctest: A challenge dataset for the open-domain machine comprehension of text. In Proceedings of the 2013 conference on empirical methods in natural language processing. 193--203.Google Scholar
- Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010. Modeling relations and their mentions without labeled text. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 148--163.Google ScholarDigital Library
- Jana Straková, Milan Straka, and Jan Hajic. 2019. Neural Architectures for Nested NER through Linearization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 5326--5331.Google ScholarCross Ref
- Jianlin Su, Ahmed Murtadha, Shengfeng Pan, Jing Hou, Jun Sun, Wanwei Huang, Bo Wen, and Yunfeng Liu. 2022. Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition. arXiv preprint arXiv:2208.03054 (2022).Google Scholar
- Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017. NewsQA: A Machine Comprehension Dataset. In Proceedings of the 2nd Workshop on Representation Learning for NLP. 191--200.Google ScholarCross Ref
- ShuohangWang and Jing Jiang. 2016. Machine comprehension using match-lstm and answer pointer. arXiv preprint arXiv:1608.07905 (2016).Google Scholar
- Juntao Yu, Bernd Bohnet, and Massimo Poesio. 2020. Named Entity Recognition as Dependency Parsing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6470--6476.Google ScholarCross Ref
- Ming Zhu, Aman Ahuja, Da-Cheng Juan, Wei Wei, and Chandan K Reddy. 2020. Question answering with long multiple-span answers. In Findings of the Association for Computational Linguistics: EMNLP 2020. 3840--3849.Google ScholarCross Ref
Index Terms
- MA-MRC: A Multi-answer Machine Reading Comprehension Dataset
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