The Neural Translation for the European Union (NTEU) engine farm enables direct machine translation for all 24 official languages of the European Union without the necessity to use a high-resourced language as a pivot. This amounts to a total of 552 translation engines for all combinations of the 24 languages. We have collected parallel data for all the language combinations publickly shared in elrc-share.eu. The translation engines have been customized to domain,for the use of the European public administrations. The delivered engines will be published in the European Language Grid. In addition to the usual automatic metrics, all the engines have been evaluated by humans based on the direct assessment methodology. For this purpose, we built an open-source platform called MTET The evaluation shows that most of the engines reach high quality and get better scores compared to an external machine translation service in a blind evaluation setup.

The Neural Translation for the European Union (NTEU) project aims to build a neural engine farm with all European official language combinations for eTranslation, without the necessity to use a high-resourced language as a pivot. NTEU started in September 2019 and will run until August 2021.

This tutorial will provide an in-depth look at the experiments, jointly carried out by KantanMT and eBay during 2018, to determine which Neural Model delivers the best translation performance for eBay Customer Service content. It will lay out the timeline, process and mechanisms used to customise Neural MT models and how these were used in conjunction with Human Based evaluations to determine which approach to Neural MT provided the best translation outcomes.The tutorial will cover the following topics and methods:- Structural differences in Neural Networks and how they assist the language decoding process – RNN, CNN and TNN will be covered in detailed.- Customisation of Neural MT using the KantanMT Platform- Using MQM Framework for the evaluation and comparison of Translation Outputs and comparison to Human Translation- Collation and analysis of experimental findings in reaching our decision to standardise on Transformer type networks.Participants of the tutorial will get a clear understanding of Neural Model types and the differences, it will also cover how to customise these models and then how to set up a controlled experiment to determine translation performance.