Datasets

12

aqua_rat

A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program that solves the question.

c3

Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple...

codah

The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design...

exams

EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations. It consists of more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.

annotations_creators: found language_creators: found languages: ar languages: bg languages: de languages: es languages: fr languages: hr languages: hu languages: it languages: lt languages: mk languages: pl languages: pt languages: sq languages: sr languages: tr languages: vi languages: bg languages: hr languages: hu languages: it languages: mk languages: pl languages: pt languages: sq languages: sr languages: ar languages: bg languages: de languages: es languages: fr languages: hr languages: hu languages: it languages: lt languages: mk languages: pl languages: pt languages: sq languages: sr languages: tr languages: vi languages: tr languages: vi languages: bg languages: hr languages: hu languages: it languages: mk languages: pl languages: pt languages: sq languages: sr languages: ar languages: bg languages: de languages: es languages: fr languages: hr languages: hu languages: it languages: lt languages: mk languages: pl languages: pt languages: sq languages: sr languages: tr languages: vi languages: tr languages: vi languages: ar languages: bg languages: de languages: es languages: fr languages: hr languages: hu languages: it languages: lt languages: mk languages: pl languages: pt languages: sq languages: sr languages: tr languages: vi languages: ar languages: bg languages: de languages: es languages: fr languages: hr languages: hu languages: it languages: lt languages: mk languages: pl languages: pt languages: sq languages: sr languages: tr languages: vi licenses: cc-by-sa-4.0 multilinguality: multilingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: multilingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: monolingual multilinguality: multilingual multilinguality: monolingual multilinguality: monolingual multilinguality: multilingual multilinguality: multilingual size_categories: 10K<n<100K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: n<1K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 10K<n<100K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: n<1K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 10K<n<100K size_categories: 1K<n<10K size_categories: 1K<n<10K size_categories: 10K<n<100K size_categories: 10K<n<100K source_datasets: original task_categories: question-answering task_ids: multiple-choice-qa

head_qa

HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio de Sanidad, Consumo y Bienestar Social. The dataset contains questions about the following topics: medicine, nu...

mc_taco

MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer pairs that require temporal commonsense comprehension. A system receives a sentence providing context information, a question designed to require temporal commonsense knowledge, and multiple candidate answers. More than one candidate answer can be plausible. The ...

piqa

To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Questi...

proto_qa

This dataset is for studying computational models trained to reason about prototypical situations. Using deterministic filtering a sampling from a larger set of all transcriptions was built. It contains 9789 instances where each instance represents a survey question from Family Feud game. Each instance exactly is a question, a set of answers, an...

pubmed_qa

PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabel...

qa_srl

The dataset contains question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence. There were 2 datsets used in the paper, newswire and wikipedia. Unfortunately the newswiredataset is built...