A collection of large datasets containing questions and their answers for use in Natural Language Processing tasks like question answering (QA).

30 июн. 2022 г. · In this paper, we investigate influential QA datasets that have been released in the era of deep learning.

A collection of datasets used in QA tasks. Solely for Natural Language Processing (NLP). Categorization based on the language. The datasets are sorted by year ...

Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question ...

The ActivityNet-QA dataset contains 58,000 human-annotated QA pairs on 5,800 videos derived from the popular ActivityNet dataset. The dataset provides a ...

A collection of medical question answering (QA) datasets.

The WikiQA corpus is a publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering. Supported ...

The Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset consisting of questions posed by crowdworkers on a set of Wikipedia articles.

This comprehensive dataset contains a wide range of theoretical questions related to computer science, covering various domains such as operating systems, ...

ShARC is a challenging QA dataset that requires logical reasoning, elements of entailment/NLI and natural language generation. Most work in machine reading ...