CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs

Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzmán, Philipp Koehn


Abstract
Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5% across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data. Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.
Anthology ID:
2020.emnlp-main.480
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5960–5969
Language:
URL:
https://aclanthology.org/2020.emnlp-main.480
DOI:
10.18653/v1/2020.emnlp-main.480
Bibkey:
Cite (ACL):
Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzmán, and Philipp Koehn. 2020. CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5960–5969, Online. Association for Computational Linguistics.
Cite (Informal):
CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs (El-Kishky et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.480.pdf
Video:
 https://slideslive.com/38939183
Data
CCAlignedWikiMatrix