no code implementations • EAMT 2020 • Joachim Van den Bogaert, Arne Defauw, Frederic Everaert, Koen Van Winckel, Alina Kramchaninova, Anna Bardadym, Tom Vanallemeersch, Pavel Smrž, Michal Hradiš
The OCCAM project (Optical Character recognition, ClassificAtion & Machine Translation) aims at integrating the CEF (Connecting Europe Facility) Automated Translation service with image classification, Translation Memories (TMs), Optical Character Recognition (OCR), and Machine Translation (MT).
no code implementations • EAMT 2020 • Joachim Van den Bogaert, Arne Defauw, Sara Szoc, Frederic Everaert, Koen Van Winckel, Alina Kramchaninova, Anna Bardadym, Tom Vanallemeersch
The CEFAT4Cities project (2020-2022) will create a “Smart Cities natural language context” (a software layer that facilitates the conversion of natural-language administrative procedures, into machine-readable data sets) on top of the existing ISA2 interoperability layer for public services.
no code implementations • EAMT 2022 • Joachim Van den Bogaert, Laurens Meeus, Alina Kramchaninova, Arne Defauw, Sara Szoc, Frederic Everaert, Koen Van Winckel, Anna Bardadym, Tom Vanallemeersch
The CEFAT4Cities project aims at creating a multilingual semantic interoperability layer for Smart Cities that allows users from all EU member States to interact with public services in their own language.
no code implementations • LREC 2020 • Arne Defauw, Tom Vanallemeersch, Koen Van Winckel, Sara Szoc, Joachim Van den Bogaert
In the context of under-resourced neural machine translation (NMT), transfer learning from an NMT model trained on a high resource language pair, or from a multilingual NMT (M-NMT) model, has been shown to boost performance to a large extent.