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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We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills.
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Russian reading comprehension with Commonsense reasoning (RuCoS) is a large-scale reading comprehension dataset that requires commonsense reasoning. RuCoS consists of queries automatically generated from CNN/Daily Mail news articles; the answer to each query is a text span from a summarizing passage of the corresponding news. The goal of RuCoS is to evaluate a machine`s ability of commonsense reasoning in reading comprehension.
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NEREL-BIO is an annotation scheme and corpus of PubMed abstracts in Russian and English. It contains annotations for 700+ Russian and 100+ English abstracts. All English PubMed annotations have corresponding Russian counterparts. NEREL-BIO comprises the following specific features: annotation of nested named entities, it can be used as a benchmark for cross-domain (NEREL -> NEREL-BIO) and cross-language (English -> Russian) transfer.
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