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We introduce a Content-based Document Alignment approach (CDA), an efficientmethod to align multilingual web documents based on content in creatingparallel training data for machine translation (MT) systems operating at theindustrial level. CDA works in two steps: (i) projecting documents of a webdomain to a shared multilingual space; then (ii) aligning them based on thesimilarity of their representations in such space. We leverage lexicaltranslation models to build vector representations using TF-IDF. CDA achievesperformance comparable with state-of-the-art systems in the WMT-16 BilingualDocument Alignment Shared Task benchmark while operating in multilingual space.Besides, we created two web-scale datasets to examine the robustness of CDA inan industrial setting involving up to 28 languages and millions of documents.The experiments show that CDA is robust, cost-effective, and is significantlysuperior in (i) processing large and noisy web data and (ii) scaling to new andlow-resourced languages.

CDA: a Cost Efficient Content-based Multilingual Web Document Aligner