# Extracting Parallel Paragraphs from Common Crawl

arXiv: https://arxiv.org/abs/1804.10413 · HTML: https://mlsanity.com/paper/1804.10413/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes](https://mlsanity.com/paper/2212.09305/) (2022)
- [RoCode: A Dataset for Measuring Code Intelligence from Problem Definitions in Romanian](https://mlsanity.com/paper/2402.13222/) (2024)

## Abstract (arXiv)

Most of the current methods for mining parallel texts from the web assume that web pages of web sites share same structure across languages. We believe that there still exists a non-negligible amount of parallel data spread across sources not satisfying this assumption. We propose an approach based on a combination of bivec (a bilingual extension of word2vec) and locality-sensitive hashing which allows us to efficiently identify pairs of parallel segments located anywhere on pages of a given web domain, regardless their structure. We validate our method on realigning segments from a large parallel corpus. Another experiment with real-world data provided by Common Crawl Foundation confirms that our solution scales to hundreds of terabytes large set of web-crawled data.