  Crawling parallel texts $\unicode{x2014}$texts that are mutual
translations$\unicode{x2014}$ from the Internet is usually done following a
brute-force approach: documents are massively downloaded in an unguided
process, and only a fraction of them end up leading to actual parallel content.
In this work we propose a smart crawling method that guides the crawl towards
finding parallel content more rapidly. Our approach builds on two different
models: one that infers the language of a document from its URL, and another
that infers whether a pair of URLs link to parallel documents. We evaluate both
models in isolation and their integration into a crawling tool. The results
demonstrate the individual effectiveness of both models and highlight that
their combination enables the early discovery of parallel content during
crawling, leading to a reduction in the amount of downloaded documents deemed
useless, and yielding a greater quantity of parallel documents compared to
conventional crawling approaches.
