Papers › A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning

A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning

15 Aug 2019IJCNLP 2019 11arXiv:1908.05514archive 2025-07-28

Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li

Rapid progress has been made in the field of reading comprehension and question answering, where several systems have achieved human parity in some simplified settings. However, the performance of these models degrades significantly when they are applied to more realistic scenarios, such as answers involve various types, multiple text strings are correct answers, or discrete reasoning abilities are required. In this paper, we introduce the Multi-Type Multi-Span Network (MTMSN), a neural reading comprehension model that combines a multi-type answer predictor designed to support various answer types (e.g., span, count, negation, and arithmetic expression) with a multi-span extraction method for dynamically producing one or multiple text spans. In addition, an arithmetic expression reranking mechanism is proposed to rank expression candidates for further confirming the prediction. Experiments show that our model achieves 79.9 F1 on the DROP hidden test set, creating new state-of-the-art results. Source code\footnote{\url{https://github.com/huminghao16/MTMSN}} is released to facilitate future work.

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NegationQuestion AnsweringReading ComprehensionReranking

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering DROP Test MTMSN Large F1 79.88 #8 of 16 Archive leaderboard report

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