Automated Translation

Tilde is at the forefront of research and innovation in neural, statistical, and hybrid machine translation. In research community Tilde is recognized by its expertise for the technologies and solutions in the field of automated translation for complex less resourced languages. Cutting-edge research results allowed Tilde to release the world’s first neural machine translation systems for smaller languages.

Automated Translation research

Internationally acknowledged research for under resourced languages

Tilde’s research in automated translation has been internationally acknowledged, particularly research for translation into morphological rich under resourced languages.

  • Development of machine translation (MT) solution for smaller morphologically rich and highly inflected languages is more complex due to relatively free word order and richness of surface forms. Many of these languages have also limited resources (parallel and monolingual corpora) that further complicates the development process. This complex set of characteristics for morphologically rich languages requires to research for these languages specially designed methods that can minimise the negative effect of the above mentioned characteristics on the MT system quality.
  • As research on neural machine translation (MT) research has been mainly focussed on widely used languages Tilde’s researchers aim to research and develop methods and algorithms for successful NN integration in MT solutions and methods for end-to-end neural machine translation system development in a context of complex less resourced languages.



Inguna Skadiņa (Tilde), Andrejs Vasiḷjevs (Tilde), Mārcis Pinnis (Tilde), Aivars Bērziņš (Tilde), Nora Aranberri, Joachim Van den Bogaert, Sally O’Connor, Mercedes García-Martínez, Iakes Goenaga, Jan Hajič, Manuel Herranz, Christian Lieske, Martin Popel, Maja Popović, Sheila Castilho, Federico Gaspari, Rudolf Rosa, Riccardo Superbo, Andy Way. 2023. Deep Dive Machine Translation. European Language Equality, Springer, 263–287.


Toms Bergmanis, Marcis Pinnis, Roberts Rozis, Jānis Šlapiņš, Valters Šics, Berta Bernāne, Guntars Pužulis, Endijs Titomers, Andre Tättar, Taido Purason, Hele-Andra Kuulmets, Agnes Luhtaru, Liisa Rätsep, Maali Tars, Annika Laumets-Tättar, Mark Fishel. 2022. MTee: Open Machine Translation Platform for Estonian Government, Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, 307–308.

Ian Roberts, Penny Labropoulou, Dimitris Galanis, Rémi Calizzano, Athanasia Kolovou, Dimitris Gkoumas, Andis Lagzdiņš (Tilde), and Stelios Piperidis. 2022. Using the European Language Grid as a ConsumerEuropean Language Grid. A Language Technology Platform for Multilingual Europe, Springer, 37-66.