This work proposes an approach to use Part-Of-Speech (POS) information to automatically detect context-dependent Translation Units (TUs) from a Translation Memory database pertaining to the customer support domain. In line with our goal to minimize context-dependency in TUs, we show how this mechanism can be deployed to create new gender-neutral and context-independent TUs. Our experiments, conducted across Portuguese (PT), Brazilian Portuguese (PT-BR), Spanish (ES), and Spanish-Latam (ES-LATAM), show that the occurrence of certain POS with specific words is accurate in identifying context dependency. In a cross-client analysis, we found that ~10% of the most frequent 13,200 TUs were context-dependent, with gender determining context-dependency in 98% of all confirmed cases. We used these findings to suggest gender-neutral equivalents for the most frequent TUs with gender constraints. Our approach is in use in the Unbabel translation pipeline, and can be integrated into any other Neural Machine Translation (NMT) pipeline.

In this work, we propose a Named Entity handling approach to improve translation quality within an existing Natural Language Processing (NLP) pipeline without modifying the Neural Machine Translation (NMT) component. Our approach seeks to enable fast delivery of such improvements and alleviate user experience problems related to NE distortion. We implement separate NE recognition and translation steps. Then, a combination of standard entity masking technique and a novel semantic equivalent placeholder guarantees that both NE translation is respected and the best overall quality is obtained from NMT. The experiments show that translation quality improves in 38.6% of the test cases when compared to a version of the NLP pipeline with less-developed NE handling capability.

We introduce a machine translation dataset for three pairs of languages in the legal domain with post-edited high-quality neural machine translation and independent human references. The data was collected as part of the EU APE-QUEST project and comprises crawled content from EU websites with translation from English into three European languages: Dutch, French and Portuguese. Altogether, the data consists of around 31K tuples including a source sentence, the respective machine translation by a neural machine translation system, a post-edited version of such translation by a professional translator, and - where available - the original reference translation crawled from parallel language websites. We describe the data collection process, provide an analysis of the resulting post-edits and benchmark the data using state-of-the-art quality estimation and automatic post-editing models. One interesting by-product of our post-editing analysis suggests that neural systems built with publicly available general domain data can provide high-quality translations, even though comparison to human references suggests that this quality is quite low. This makes our dataset a suitable candidate to test evaluation metrics. The data is freely available as an ELRC-SHARE resource.