mlsanityData to 7 Oct 2026

Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension

arXiv 1904.09679 ↗First posted 21 Apr 2019cs.CLcited by 80

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Abstract (arXiv)

Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. C^3 is available at https://dataset.org/c3/.

Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2019.

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Citing papers per year: 2021: 11; 2022: 3; 2023: 15; 2024: 21; 2025: 17; 2026: 13.

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