Filtering and routing multilingual documents for translation

Translation is a key capability to access relevant information expressed in various languages on social media. Unfortunately, systematically translating all content far exceeds the capacity of most organizations. Computer-aided translation (CAT) tools can significantly increase the productivity of translators, but can not ultimately cope with the overwhelming amount of content to translate. In this contribution, we describe and experiment with an approach where we use the structure in a corpus to adequately route the content to the proper workflow, including translators, CAT tools or purely automatic approaches. We show that linguistically motivated structure such as document genre can help decide on the proper translation workflow. However, automatically discovered structure has an effect that is at least as important and allows us to define groups of documents that may be translated automatically with reasonable output quality. This suggests that computational intelligence models that can efficiently organize document collection will provide increased capability to access textual content from various target languages.