Smart bilingual focused crawling of parallel documents

Abstract Crawling parallel texts—texts that are mutual translations—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. We follow a neural approach that consists in adapting a pre-trained multilingual language model based on the encoder of the Transformer architecture by fine-tuning it for two new tasks: inferring the language of a document from its Uniform Resource Locator (URL) and inferring 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 us to address a practical engineering challenge: the early discovery of parallel content during web crawling in a given language pair. This leads to a reduction in the amount of downloaded documents deemed useless and yields a greater quantity of parallel documents compared to conventional crawling approaches.