Towards Using Web-Crawled Data for Domain Adaptation in Statistical Machine Translation

This paper reports on the ongoing work focused on domain adaptation of statistical machine translation using domain-specific data obtained by domain-focused web crawling. We present a strategy for crawling monolingual and parallel data and their exploitation for testing, language modelling, and system tuning in a phrase-based machine translation framework. The proposed approach is evaluated on the domains of Natural Environment and Labour Legislation and two language pairs: English–French and English–Greek.