no code implementations • 25 Apr 2013 • Daan Fierens, Guy Van Den Broeck, Joris Renkens, Dimitar Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, Luc De Raedt
This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs.
no code implementations • 17 Apr 2018 • Alberto Poncelas, Dimitar Shterionov, Andy Way, Gideon Maillette de Buy Wenniger, Peyman Passban
A prerequisite for training corpus-based machine translation (MT) systems -- either Statistical MT (SMT) or Neural MT (NMT) -- is the availability of high-quality parallel data.
no code implementations • 23 Feb 2019 • Eva Vanmassenhove, Amit Moryossef, Alberto Poncelas, Andy Way, Dimitar Shterionov
In contradiction with the results described in previous comparable shared tasks, our neural models performed better than our best traditional approaches with our best feature set-up.
no code implementations • WS 2019 • Eva Vanmassenhove, Dimitar Shterionov, Andy Way
This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT).
no code implementations • WS 2019 • Dimitar Shterionov, Joachim Wagner, F{\'e}lix do Carmo
Automatic post-editing (APE) can be reduced to a machine translation (MT) task, where the source is the output of a specific MT system and the target is its post-edited variant.
no code implementations • RANLP 2019 • Alberto Poncelas, Maja Popovi{\'c}, Dimitar Shterionov, Gideon Maillette de Buy Wenniger, Andy Way
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training.
no code implementations • 9 Sep 2019 • Alberto Poncelas, Maja Popovic, Dimitar Shterionov, Gideon Maillette de Buy Wenniger, Andy Way
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training.
no code implementations • LREC 2020 • Piyush Arora, Dimitar Shterionov, Yasufumi Moriya, Abhishek Kaushik, Daria Dzendzik, Gareth Jones
In this paper we devote special attention to the automatic translation (AT) component which is crucial for the overall quality of the MMCLIR system.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +6
no code implementations • ACL 2020 • Xabier Soto, Dimitar Shterionov, Alberto Poncelas, Andy Way
Machine translation (MT) has benefited from using synthetic training data originating from translating monolingual corpora, a technique known as backtranslation.
no code implementations • EACL 2021 • Eva Vanmassenhove, Dimitar Shterionov, Matthew Gwilliam
Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data.
no code implementations • 13 Sep 2021 • Eva Vanmassenhove, Chris Emmery, Dimitar Shterionov
Recent years have seen an increasing need for gender-neutral and inclusive language.
1 code implementation • 11 Dec 2021 • Javad PourMostafa Roshan Sharami, Dimitar Shterionov, Pieter Spronck
We then select the top K sentences with the highest similarity score to train a new machine translation system tuned to the specific in-domain data.
no code implementations • 7 Feb 2022 • Mathieu De Coster, Dimitar Shterionov, Mieke Van Herreweghe, Joni Dambre
Automatic translation from signed to spoken languages is an interdisciplinary research domain, lying on the intersection of computer vision, machine translation and linguistics.
1 code implementation • 19 Feb 2023 • Ali Boluki, Javad PourMostafa Roshan Sharami, Dimitar Shterionov
However, only a few reviews ever receive any helpfulness votes on online marketplaces.
1 code implementation • 18 Apr 2023 • Javad PourMostafa Roshan Sharami, Dimitar Shterionov, Frédéric Blain, Eva Vanmassenhove, Mirella De Sisto, Chris Emmery, Pieter Spronck
While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data.
no code implementations • EAMT 2022 • Elena Murgolo, Javad PourMostafa Roshan Sharami, Dimitar Shterionov
In the latter, a quality estimate of the translation output can guide the human post-editor or even make rough approximations of the post-editing effort.
no code implementations • EAMT 2022 • Dimitar Shterionov, Mirella De Sisto, Vincent Vandeghinste, Aoife Brady, Mathieu De Coster, Lorraine Leeson, Josep Blat, Frankie Picron, Marcello Paolo Scipioni, Aditya Parikh, Louis ten Bosh, John O’Flaherty, Joni Dambre, Jorn Rijckaert
The SignON project (www. signon-project. eu) focuses on the research and development of a Sign Language (SL) translation mobile application and an open communications framework.
no code implementations • LREC 2022 • Mirella De Sisto, Vincent Vandeghinste, Santiago Egea Gómez, Mathieu De Coster, Dimitar Shterionov, Horacio Saggion
Furthermore, we propose a framework to address the lack of standardization at format level, unify the available resources and facilitate SL research for different languages.
no code implementations • EMNLP 2021 • Eva Vanmassenhove, Chris Emmery, Dimitar Shterionov
Recent years have seen an increasing need for gender-neutral and inclusive language.
no code implementations • LChange (ACL) 2022 • Marije Timmermans, Eva Vanmassenhove, Dimitar Shterionov
From the analysis, it appears that “natie”, “volk” and “vaderlan”’ became more nationalistically-loaded over time.
no code implementations • MTSummit 2021 • Mirella De Sisto, Dimitar Shterionov, Irene Murtagh, Myriam Vermeerbergen, Lorraine Leeson
This paper addresses the tasks of sign segmentation and segment-meaning mapping in the context of sign language (SL) recognition.