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