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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The VNHSGE (VietNamese High School Graduation Examination) dataset, developed exclusively for evaluating large language models (LLMs), is introduced in this article. The dataset, which covers nine subjects, was generated from the Vietnamese National High School Graduation Examination and comparable tests. 300 literary essays have been included, and there are over 19,000 multiple-choice questions on a range of topics. The dataset assesses LLMs in multitasking situations such as question answering, text generation, reading comprehension, visual question answering, and more by including both textual data and accompanying images. Using ChatGPT and BingChat, we evaluated LLMs on the VNHSGE dataset and contrasted their performance with that of Vietnamese students to see how well they performed. The results show that ChatGPT and BingChat both perform at a human level in a number of areas, including literature, English, history, geography, and civics education. They still have space to grow, t
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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.
Jericho Environment Commonsense Comprehension (JECC) is a dataset for commonsense reasoning. It consists of 29 games in multiple domains from the Jericho Environment hausknecht2019interactive.
CodeQueries Benchmark dataset consists of instances of semantic queries, code context and code spans in the context corresponding to the semantic queries. The dataset can be used in experiments involving semantic query comprehension with an extractive question-answering methodology over code. More details can be found in the paper.
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RadQA is a radiology question answering dataset with 3074 questions posed against radiology reports and annotated with their corresponding answer spans (resulting in a total of 6148 question-answer evidence pairs) by physicians. The questions are manually created using the clinical referral section of the reports that take into account the actual information needs of ordering physicians and eliminate bias from seeing the answer context (and, further, organically create unanswerable questions). The answer spans are marked within the Findings and Impressions sections of a report. The dataset aims to satisfy the complex clinical requirements by including complete (yet concise) answer phrases (which are not just entities) that can span multiple lines.
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ConcurrentQA is a textual multi-hop QA benchmark to require concurrent retrieval over multiple data-distributions (i.e. Wikipedia and email data). The dataset follow the exact same schema and design as HotpotQA. The data set is downloadable here: https://github.com/facebookresearch/concurrentqa. It also contains model and result analysis code. This benchmark can also be used to study privacy when reasoning over data distributed in multiple privacy scopes --- i.e. Wikipedia in the public domain and emails in the private domain.
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Tasks Our shared task has three subtasks. Subtask 1 and 2 focus on evaluating machine learning models' performance with regard to two definitions of abstractness (Spreen and Schulz, 1966; Changizi, 2008), which we call imperceptibility and nonspecificity, respectively. Subtask 3 aims to provide some insights to their relationships.
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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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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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QReCC contains 14K conversations with 81K question-answer pairs. QReCC is built on questions from TREC CAsT, QuAC and Google Natural Questions. While TREC CAsT and QuAC datasets contain multi-turn conversations, Natural Questions is not a conversational dataset. Questions in NQ dataset were used as prompts to create conversations explicitly balancing types of context-dependent questions, such as anaphora (co-references) and ellipsis.
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TyDi QA is a question answering dataset covering 11 typologically diverse languages with 200K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology — the set of linguistic features that each language expresses — such that the authors expect models performing well on this set to generalize across a large number of the languages in the world.
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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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We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop.
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Shmoop Corpus is a dataset of 231 stories that are paired with detailed multi-paragraph summaries for each individual chapter (7,234 chapters), where the summary is chronologically aligned with respect to the story chapter. From the corpus, a set of common NLP tasks are constructed, including Cloze-form question answering and a simplified form of abstractive summarization, as benchmarks for reading comprehension on stories.
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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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QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
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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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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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ROPES is a QA dataset which tests a system's ability to apply knowledge from a passage of text to a new situation. A system is presented a background passage containing a causal or qualitative relation(s), a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the back-ground passage in the context of the situation.
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X-WikiRE is a new, large-scale multilingual relation extraction dataset in which relation extraction is framed as a problem of reading comprehension to allow for generalization to unseen relations.
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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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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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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CosmosQA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people’s everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context.
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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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emrQA has 1 million question-logical form and 400,000+ questionanswer evidence pairs.
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The ProPara dataset is designed to train and test comprehension of simple paragraphs describing processes (e.g., photosynthesis), designed for the task of predicting, tracking, and answering questions about how entities change during the process.
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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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CliCR is a new dataset for domain specific reading comprehension used to construct around 100,000 cloze queries from clinical case reports.
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WiC: The Word-in-Context Dataset A reliable benchmark for the evaluation of context-sensitive word embeddings.
Worldtree is a corpus of explanation graphs, explanatory role ratings, and associated tablestore. It contains explanation graphs for 1,680 questions, and 4,950 tablestore rows across 62 semi-structured tables are provided. This data is intended to be paired with the AI2 Mercury Licensed questions.
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The Cloze Test by Teachers (CLOTH) benchmark is a collection of nearly 100,000 4-way multiple-choice cloze-style questions from middle- and high school-level English language exams, where the answer fills a blank in a given text. Each question is labeled with a type of deep reasoning it involves, where the four possible types are grammar, short-term reasoning, matching/paraphrasing, and long-term reasoning, i.e., reasoning over multiple sentences
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CoQA is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation.
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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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HotpotQA is a question answering dataset collected on the English Wikipedia, containing about 113K crowd-sourced questions that are constructed to require the introduction paragraphs of two Wikipedia articles to answer. Each question in the dataset comes with the two gold paragraphs, as well as a list of sentences in these paragraphs that crowdworkers identify as supporting facts necessary to answer the question.
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MCScript is used as the official dataset of SemEval2018 Task11. This dataset constructs a collection of text passages about daily life activities and a series of questions referring to each passage, and each question is equipped with two answer choices. The MCScript comprises 9731, 1411, and 2797 questions in training, development, and test set respectively.
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MultiRC (Multi-Sentence Reading Comprehension) is a dataset of short paragraphs and multi-sentence questions, i.e., questions that can be answered by combining information from multiple sentences of the paragraph. The dataset was designed with three key challenges in mind: * The number of correct answer-options for each question is not pre-specified. This removes the over-reliance on answer-options and forces them to decide on the correctness of each candidate answer independently of others. In other words, the task is not to simply identify the best answer-option, but to evaluate the correctness of each answer-option individually. * The correct answer(s) is not required to be a span in the text. * The paragraphs in the dataset have diverse provenance by being extracted from 7 different domains such as news, fiction, historical text etc., and hence are expected to be more diverse in their contents as compared to single-domain datasets. The entire corpus consists of around 10K questions
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Question Answering in Context is a large-scale dataset that consists of around 14K crowdsourced Question Answering dialogs with 98K question-answer pairs in total. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text.
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ShARC is a Conversational Question Answering dataset focussing on question answering from texts containing rules.
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The NarrativeQA dataset includes a list of documents with Wikipedia summaries, links to full stories, and questions and answers.
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With the same format as WikiHop, the MedHop dataset is based on research paper abstracts from PubMed, and the queries are about interactions between pairs of drugs. The correct answer has to be inferred by combining information from a chain of reactions of drugs and proteins.
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The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided.
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The TextbookQuestionAnswering (TQA) dataset is drawn from middle school science curricula. It consists of 1,076 lessons from Life Science, Earth Science and Physical Science textbooks. This includes 26,260 questions, including 12,567 that have an accompanying diagram.