NAACL 2022findings13 citations
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training
Patrick Huber, Armen Aghajanyan, Barlas Oguz, Dmytro Okhonko, Scott Yih, Sonal Gupta, Xilun Chen
Abstract
We propose a novel open-domain question-answering dataset based on the Common Crawl project. With a previously unseen number of around 130 million multilingual question-answer pairs (including about 60 million English data-points), we use our large-scale, natural, diverse and high-quality corpus to in-domain pre-train popular language models for the task of question-answering. In our experiments, we find that our Common Crawl Question Answering dataset (CCQA) achieves promising results in zero-shot, low resource and fine-tuned settings across multiple tasks, models and benchmarks.
BibTeX
@inproceedings{huber-etal-2022-ccqa,
title = "{CCQA}: A New Web-Scale Question Answering Dataset for Model Pre-Training",
author = "Huber, Patrick and
Aghajanyan, Armen and
Oguz, Barlas and
Okhonko, Dmytro and
Yih, Scott and
Gupta, Sonal and
Chen, Xilun",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.184/",
doi = "10.18653/v1/2022.findings-naacl.184",
pages = "2402--2420"
}