Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems.
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JEC-QA is a LQA (Legal Question Answering) dataset collected from the National Judicial Examination of China. It contains 26,365 multiple-choice and multiple-answer questions in total. The task of the dataset is to predict the answer using the questions and relevant articles. To do well on JEC-QA, both retrieving and answering are important.
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XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently, the dataset is entirely parallel across 11 languages.
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BiPaR is a manually annotated bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support monolingual, multilingual and cross-lingual reading comprehension on novels. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written in parallel in two languages. BiPaR is diverse in prefixes of questions, answer types and relationships between questions and passages. Answering the questions requires reading comprehension skills of coreference resolution, multi-sentence reasoning, and understanding of implicit causality.
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XQA is a data which consists of a total amount of 90k question-answer pairs in nine languages for cross-lingual open-domain question answering.
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ChID is a large-scale Chinese IDiom dataset for cloze test. ChID contains 581K passages and 729K blanks, and covers multiple domains. In ChID, the idioms in a passage were replaced with blank symbols. For each blank, a list of candidate idioms including the golden idiom are provided as choice.
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CMRC is a dataset is annotated by human experts with near 20,000 questions as well as a challenging set which is composed of the questions that need reasoning over multiple clues.
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Delta Reading Comprehension Dataset (DRCD) is an open domain traditional Chinese machine reading comprehension (MRC) dataset. This dataset aimed to be a standard Chinese machine reading comprehension dataset, which can be a source dataset in transfer learning. The dataset contains 10,014 paragraphs from 2,108 Wikipedia articles and 30,000+ questions generated by annotators.
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DuReader is a large-scale open-domain Chinese machine reading comprehension dataset. The dataset consists of 200K questions, 420K answers and 1M documents. The questions and documents are based on Baidu Search and Baidu Zhidao. The answers are manually generated. The dataset additionally provides question type annotations – each question was manually annotated as either Entity, Description or YesNo and one of Fact or Opinion.
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C3 is a free-form multiple-Choice Chinese machine reading Comprehension dataset.
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CMRC 2019 is a Chinese Machine Reading Comprehension dataset that was used in The Third Evaluation Workshop on Chinese Machine Reading Comprehension. Specifically, CMRC 2019 is a sentence cloze-style machine reading comprehension dataset that aims to evaluate the sentence-level inference ability.
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This dataset is constructed and based on the online free-access fictions that are tagged with sci-fi, urban novel, love story, youth, etc. It is used for Writing Polishment with Smile (WPS) a task that aims to polish plain text with similes. All similes are extracted by rich regular expression, and the extraction precision is estimated as 92% by labelling 500 random extracted samples. It contains 5M samples for training and 2.5k for validation and test respectively.
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A high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization.
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