This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.
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PoseScript is a dataset that pairs a few thousand 3D human poses from AMASS with rich human-annotated descriptions of the body parts and their spatial relationships. This dataset is designed for the retrieval of relevant poses from large-scale datasets and synthetic pose generation, both based on a textual pose description.
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Perception Test is a benchmark designed to evaluate the perception and reasoning skills of multimodal models. It introduces real-world videos designed to show perceptually interesting situations and defines multiple tasks that require understanding of memory, abstract patterns, physics, and semantics – across visual, audio, and text modalities. The benchmark consists of 11.6k videos, 23s average length, filmed by around 100 participants worldwide. The videos are densely annotated with six types of labels: object and point tracks, temporal action and sound segments, multiple-choice video question-answers and grounded video question-answers. The benchmark probes pre-trained models for their transfer capabilities, in a zero-shot / few-shot or fine tuning regime.
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EgoTask QA benchmark contains 40K balanced question-answer pairs selected from 368K programmatically generated questions generated over 2K egocentric videos. It provides a single home for the crucial dimensions of task understanding through question-answering on real-world egocentric videos.
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The Bamboogle dataset is a collection of questions that was constructed to investigate the ability of language models to perform compositional reasoning tasks. The dataset is made up of questions that Google answers incorrectly. It covers many different types of questions on various areas, written in unique ways.
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ConvFinQA is a dataset designed to study the chain of numerical reasoning in conversational question answering. The dataset contains 3892 conversations containing 14115 questions where 2715 of the conversations are simple conversations, and the rest 1,177 are hybrid conversations.
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MINTAKA is a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. It is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers.
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PrOntoQA is a question-answering dataset which generates examples with chains-of-thought that describe the reasoning required to answer the questions correctly. The sentences in the examples are syntactically simple and amenable to semantic parsing. It can be used to formally analyze the predicted chain-of-thought from large language models such as GPT-3.
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Science Question Answering (ScienceQA) is a new benchmark that consists of 21,208 multimodal multiple choice questions with diverse science topics and annotations of their answers with corresponding lectures and explanations. Out of the questions in ScienceQA, 10,332 (48.7%) have an image context, 10,220 (48.2%) have a text context, and 6,532 (30.8%) have both. Most questions are annotated with grounded lectures (83.9%) and detailed explanations (90.5%). The lecture and explanation provide general external knowledge and specific reasons, respectively, for arriving at the correct answer. To the best of our knowledge, ScienceQA is the first large-scale multimodal dataset that annotates lectures and explanations for the answers.
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The dataset comprises 4,500 question-answer pairs collected from trusted medical sources, with at least one answer and at most four unique paraphrased answers per question
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CLEVR-Math is a multi-modal math word problems dataset consisting of simple math word problems involving addition/subtraction, represented partly by a textual description and partly by an image illustrating the scenario. These word problems requires a combination of language, visual and mathematical reasoning.
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ChiQA is a dataset designed for visual question answering tasks that not only measures the relatedness but also measures the answerability, which demands more fine-grained vision and language reasoning. It contains more than 40K questions and more than 200K question-images pairs. The questions are real-world image-independent queries that are more various and unbiased.
CC-Riddle is a Chinese character riddle dataset covering the majority of common simplified Chinese characters by crawling riddles from the Web and generating brand new ones. In the generation stage, the authors provide the Chinese phonetic alphabet, decomposition and explanation of the solution character for the generation model and get multiple riddle descriptions for each tested character. Then the generated riddles are manually filtered and the final dataset, CCRiddle is composed of both human-written riddles and filtered generated riddle.
Contains 1507 domain-expert annotated consumer health questions and corresponding summaries. The dataset is derived from the community question answering forum and therefore provides a valuable resource for understanding consumer health-related posts on social media.
Phrase in Context is a curated benchmark for phrase understanding and semantic search, consisting of three tasks of increasing difficulty: Phrase Similarity (PS), Phrase Retrieval (PR) and Phrase Sense Disambiguation (PSD). The datasets are annotated by 13 linguistic experts on Upwork and verified by two groups: ~1000 AMT crowdworkers and another set of 5 linguistic experts. PiC benchmark is distributed under CC-BY-NC 4.0.
DiSCQ is a newly curated question dataset composed of 2,000+ questions paired with the snippets of text (triggers) that prompted each question. The questions are generated by medical experts from 100+ MIMIC-III discharge summaries. This dataset is released to facilitate further research into realistic clinical Question Answering (QA) and Question Generation (QG).
JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese.
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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.
QAMPARI is an ODQA benchmark, where question answers are lists of entities, spread across many paragraphs. It was created by (a) generating questions with multiple answers from Wikipedia's knowledge graph and tables, (b) automatically pairing answers with supporting evidence in Wikipedia paragraphs, and (c) manually paraphrasing questions and validating each answer.
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Contains over 70,000 question-answer pairs from both structured tables and unstructured notes from a publicly available Electronic Health Record (EHR).
FairytaleQA is a dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Annotated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly story narratives, covering seven types of narrative elements or relations. It can support narrative Question Generation (QG) and Narrative Question Answering (QA) tasks.
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The large-scale MUSIC-AVQA dataset of musical performance contains 45,867 question-answer pairs, distributed in 9,288 videos for over 150 hours. All QA pairs types are divided into 3 modal scenarios, which contain 9 question types and 33 question templates. Finally, as an open-ended problem of our AVQA tasks, all 42 kinds of answers constitute a set for selection.
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The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
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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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A Benchmark for Robust Multi-Hop Spatial Reasoning in Texts
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MuLD (Multitask Long Document Benchmark) is a set of 6 NLP tasks where the inputs consist of at least 10,000 words. The benchmark covers a wide variety of task types including translation, summarization, question answering, and classification. Additionally there is a range of output lengths from a single word classification label all the way up to an output longer than the input text.
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Persian Question Answering Dataset (PQuAD) is a crowdsourced reading comprehension dataset on Persian Wikipedia articles. It includes 80,000 questions along with their answers, with 25% of the questions being adversarially unanswerable.
A large set of questions and answers about the ocean and the Brazilian coast both in Portuguese and English. Pirá is a crowdsourced question answering (QA) dataset on the ocean and the Brazilian coast designed for reading comprehension.
JaQuAD (Japanese Question Answering Dataset) is a question answering dataset in Japanese that consists of 39,696 extractive question-answer pairs on Japanese Wikipedia articles.
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QALD-9-Plus Dataset Description QALD-9-Plus is the dataset for Knowledge Graph Question Answering (KGQA) based on well-known QALD-9.
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SCROLLS (Standardized CompaRison Over Long Language Sequences) is an NLP benchmark consisting of a suite of tasks that require reasoning over long texts. SCROLLS contains summarization, question answering, and natural language inference tasks, covering multiple domains, including literature, science, business, and entertainment. The dataset is made available in a unified text-to-text format and host a live leaderboard to facilitate research on model architecture and pretraining methods.
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QuALITY (Question Answering with Long Input Texts, Yes!) is a multiple-choice question answering dataset for long document comprehension. The dataset consists of context passages in English that have an average length of about 5,000 tokens, much longer than typical current models can process. Unlike in prior work with passages, the questions are written and validated by contributors who have read the entire passage, rather than relying on summaries or excerpts.
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We present the AWS documentation corpus, an open-book QA dataset, which contains 25,175 documents along with 100 matched questions and answers. These questions are inspired by the author's interactions with real AWS customers and the questions they asked about AWS services. The data was anonymized and aggregated. All questions in the dataset have a valid, factual and unambiguous answer within the accompanying documents, we deliberately avoided questions that are ambiguous, incomprehensible, opinion-seeking, or not clearly a request for factual information. All questions, answers and accompanying documents in the dataset are annotated by authors. There are two types of answers: text and yes-no-none(YNN) answers. Text answers range from a few words to a full paragraph sourced from a continuous block of words in a document or from different locations within the same document. Every question in the dataset has a matched text answer. Yes-no-none(YNN) answers can be yes, no, or none dependin
Multitask learning has led to significant advances in Natural Language Processing, including the decaNLP benchmark where question answering is used to frame 10 natural language understanding tasks in a single model. PQ-decaNLP is a crowd-sourced corpus of paraphrased questions, annotated with paraphrase phenomena. This enables analysis of how transformations such as swapping the class labels and changing the sentence modality lead to a large performance degradation.
ConditionalQA is a Question Answering (QA) dataset that contains complex questions with conditional answers, i.e. the answers are only applicable when certain conditions apply.
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TopiOCQA (pronounced Tapioca) is an open-domain conversational dataset with topic switches on Wikipedia. TopiOCQA contains 3,920 conversations with information-seeking questions and free-form answers. On average, a conversation in the dataset spans 13 question-answer turns and involves four topics (documents). TopiOCQA poses a challenging test-bed for models, where efficient retrieval is required on multiple turns of the same conversation, in conjunction with constructing valid responses using conversational history.
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SCIMAT is a large question-answer dataset for mathematics and science problems; such dataset can have impact on online education, intelligent tutoring and automated grading.
MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents.
SituatedQA is an open-retrieval QA dataset where systems must produce the correct answer to a question given the temporal or geographical context. Answers to the same question may change depending on the extralinguistic contexts (when and where the question was asked).
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TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. The authors crafted questions that some humans would answer falsely due to a false belief or misconception.
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FinQA is a new large-scale dataset with Question-Answering pairs over Financial reports, written by financial experts. The dataset contains 8,281 financial QA pairs, along with their numerical reasoning processes.
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AIT-QA is a dataset for Table Question Answering (Table-QA) which is specific to the airline industry. The dataset consists of 515 questions authored by human annotators on 116 tables extracted from public U.S. SEC filings of major airline companies for the fiscal years 2017-2019. It also contains annotations pertaining to the nature of questions, marking those that require hierarchical headers, domain-specific terminology, and paraphrased forms.
KaggleDBQA is a challenging cross-domain and complex evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions.
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NExT-QA is a VideoQA benchmark targeting the explanation of video contents. It challenges QA models to reason about the causal and temporal actions and understand the rich object interactions in daily activities. It supports both multi-choice and open-ended QA tasks. The videos are untrimmed and the questions usually invoke local video contents for answers.
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Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as a source of distractors.
The PROST (Physical Reasoning about Objects Through Space and Time) dataset contains 18,736 multiple-choice questions made from 14 manually curated templates, covering 10 physical reasoning concepts. All questions are designed to probe both causal and masked language models in a zero-shot setting.
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CRONQUESTIONS, the Temporal KGQA dataset consists of two parts: a KG with temporal annotations, and a set of natural language questions requiring temporal reasoning.
We take advantage of the ground truth of NLVR images, design CFGs to generate stories, and use spatial reasoning rules to ask and answer spatial reasoning questions. This automatically generated data is called SpaRTQA. https://aclanthology.org/2021.naacl-main.364/