MCTest is a freely available set of stories and associated questions intended for research on the machine comprehension of text.
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The MRQA (Machine Reading for Question Answering) dataset is a dataset for evaluating the generalization capabilities of reading comprehension systems.
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Children’s Book Test (CBT) is designed to measure directly how well language models can exploit wider linguistic context. The CBT is built from books that are freely available thanks to Project Gutenberg.
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Logical reasoning is an important ability to examine, analyze, and critically evaluate arguments as they occur in ordinary language as the definition from Law School Admission Council. ReClor is a dataset extracted from logical reasoning questions of standardized graduate admission examinations.
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DREAM is a multiple-choice Dialogue-based REAding comprehension exaMination dataset. In contrast to existing reading comprehension datasets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding.
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LogiQA consists of 8,678 QA instances, covering multiple types of deductive reasoning. Results show that state-of-the-art neural models perform by far worse than human ceiling. The dataset can also serve as a benchmark for reinvestigating logical AI under the deep learning NLP setting.
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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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MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is modified from Chinese high school English listening comprehension test data. It tests dialogue reasoning via next utterance prediction.
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Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard coreferences before selecting the appropriate span(s) in the paragraphs for answering questions.
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CMRC 2018 is a dataset for Chinese Machine Reading Comprehension. Specifically, it is a span-extraction reading comprehension dataset that is similar to SQuAD.
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DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie.
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C3 is a free-form multiple-Choice Chinese machine reading Comprehension dataset.
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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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Holl-E is a dataset containing movie chats wherein each response is explicitly generated by copying and/or modifying sentences from unstructured background knowledge such as plots, comments and reviews about the movie.
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We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop.
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Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain unambiguous annotations for all datasets. Furthermore, we build an evaluation system and carefully choose evaluations metrics for every task, thus establishing fair comparison across Korean language models.
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With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effectiveness of many applications that rely on real-time knowledge. While previous question answering (QA) datasets have concentrated on formal text like news and Wikipedia, the first large-scale dataset for QA over social media data is presented. To make sure the tweets are meaningful and contain interesting information, tweets used by journalists to write news articles are gathered. Then human annotators are asked to write questions and answers upon these tweets. Unlike other QA datasets like SQuAD in which the answers are extractive, the answer are allowed to be abstractive. The task requires model to read a short tweet and a question and outputs a text phrase (does not need to be in the tweet) as the answer.
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Who-did-What collects its corpus from news and provides options for questions similar to CBT. Each question is formed from two independent articles: an article is treated as context to be read and a separate article on the same event is used to form the query.
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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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A large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018).
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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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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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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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The UIT-ViWikiQA is a dataset for evaluating sentence extraction-based machine reading comprehension in the Vietnamese language. The UIT-ViWikiQA dataset is converted from the UIT-ViQuAD dataset, consisting of 23,074 question-answers based on 5,109 passages of 174 Vietnamese articles from Wikipedia.
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ExpMRC is a benchmark for the Explainability evaluation of Machine Reading Comprehension. ExpMRC contains four subsets of popular MRC datasets with additionally annotated evidences, including SQuAD, CMRC 2018, RACE+ (similar to RACE), and C3, covering span-extraction and multiple-choice questions MRC tasks in both English and Chinese.
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MAUD is an expert-annotated merger agreement reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points study, where lawyers and law students answered 92 questions about 152 merger agreements.
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IDK-MRC is an Indonesian Machine Reading Comprehension (MRC) dataset consists of more than 10K questions in total with over 5K unanswerable questions with diverse question types.
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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.
PersianQA: a dataset for Persian Question Answering Persian Question Answering (PersianQA) Dataset is a reading comprehension dataset on Persian Wikipedia. The crowd-sourced the dataset consists of more than 9,000 entries. Each entry can be either an impossible-to-answer or a question with one or more answers spanning in the passage (the context) from which the questioner proposed the question. Much like the SQuAD2.0 dataset, the impossible or unanswerable questions can be utilized to create a system which "knows that it doesn't know the answer".
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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.
Large scale machine reading comprehension dataset in Urdu language.