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This paper presents BiPaR, a bilingual parallel novel-style machine readingcomprehension (MRC) dataset, developed to support multilingual andcross-lingual reading comprehension. The biggest difference between BiPaR andexisting reading comprehension datasets is that each triple (Passage, Question,Answer) in BiPaR is written parallelly in two languages. We collect 3,667bilingual parallel paragraphs from Chinese and English novels, from which weconstruct 14,668 parallel question-answer pairs via crowdsourced workersfollowing a strict quality control procedure. We analyze BiPaR in depth andfind that BiPaR offers good diversification in prefixes of questions, answertypes and relationships between questions and passages. We also observe thatanswering questions of novels requires reading comprehension skills ofcoreference resolution, multi-sentence reasoning, and understanding of implicitcausality, etc. With BiPaR, we build monolingual, multilingual, andcross-lingual MRC baseline models. Even for the relatively simple monolingualMRC on this dataset, experiments show that a strong BERT baseline is over 30points behind human in terms of both EM and F1 score, indicating that BiPaRprovides a challenging testbed for monolingual, multilingual and cross-lingualMRC on novels. The dataset is available at https://multinlp.github.io/BiPaR/.

BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels