  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.
