no code implementations • EAMT 2022 • Alina Kramchaninova, Arne Defauw
We combine the synthetic data with non-synthetic data (SQuAD 2. 0) and evaluate multilingual BERT models on the question answering task.
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 2022 • Tom Vanallemeersch, Arne Defauw, Sara Szoc, Alina Kramchaninova, Joachim Van den Bogaert, Andrea Lösch
We describe the language technology (LT) assessments carried out in the ELRC action (European Language Resource Coordination) of the European Commission, which aims towards minimising language barriers across the EU.
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 • 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.