Commercial Machine Translation (MT) providers offer functionalities that allow users to leverage bilingual glossaries. This poses the question of how to turn glossaries that were intended to be used by a human translator into MT-ready ones, removing entries that could harm the MT output. We present two automatic filtering approaches - one based on rules and the second one relying on a translation memory - and a manual filtering procedure carried out by a linguist. The resulting glossaries are added to the MT model. The outputs are compared against a baseline where no glossary is used and an output produced using the original glossary. The present work aims at investigating if any of these filtering methods can bring a higher terminology accuracy without negative effects on the overall quality. Results are measured with terminology accuracy and Translation Edit Rate. We test our filters on two language pairs, En-Fr and De-En. Results show that some of the automatically filtered glossaries improve the output when compared to the baseline, and they may help reach a better balance between accuracy and overall quality, replacing the costly manual process without quality loss.

Recently, a number of commercial Machine Translation (MT) providers have started to offer glossary features allowing users to enforce terminology into the output of a generic model. However, to the best of our knowledge it is not clear how such features would impact terminology accuracy and the overall quality of the output. The present contribution aims at providing a first insight into the performance of the glossary-enhanced generic models offered by four providers. Our tests involve two different domains and language pairs, i.e. Sportswear En–Fr and Industrial Equipment De–En. The output of each generic model and of the glossaryenhanced one will be evaluated relying on Translation Error Rate (TER) to take into account the overall output quality and on accuracy to assess the compliance with the glossary. This is followed by a manual evaluation. The present contribution mainly focuses on understanding how these glossary features can be fruitfully exploited by language service providers (LSPs), especially in a scenario in which a customer glossary is already available and is added to the generic model as is.

We propose a novel method to bootstrap the construction of parallel corpora for new pairs of structurally different languages. We do so by combining the use of a pivot language and self-training. A pivot language enables the use of existing translation models to bootstrap the alignment and a self-training procedure enables to achieve better alignment, both at the document and sentence level. We also propose several evaluation methods for the resulting alignment.