no code implementations • EAMT 2020 • Maja Popovic
This project aims to identify the important aspects of translation quality of user reviews which will represent a starting point for developing better automatic MT metrics and challenge test sets, and will be also helpful for developing MT systems for this genre.
no code implementations • EAMT 2020 • Maja Popovic
Many studies have confirmed that translated texts exhibit different features than texts originally written in the given language.
no code implementations • AMTA 2022 • Pintu Lohar, Sinead Madden, Edmond O’Connor, Maja Popovic, Tanya Habruseva
Moreover, we developed a first-ever test parallel data set of product descriptions.
1 code implementation • MTSummit 2021 • Maja Popovic
This work describes analysis of nature and causes of MT errors observed by different evaluators under guidance of different quality criteria: adequacy and comprehension and and a not specified generic mixture of adequacy and fluency.
1 code implementation • ACL (GeBNLP) 2021 • Nishtha Jain, Maja Popovic, Declan Groves, Eva Vanmassenhove
The method can be applied both for creating gender balanced outputs as well as for creating gender balanced training data.
no code implementations • 1 May 2020 • Andy Way, Rejwanul Haque, Guodong Xie, Federico Gaspari, Maja Popovic, Alberto Poncelas
Every day, more people are becoming infected and dying from exposure to COVID-19.
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 2018 • Iris Hendrickx, Eirini Takoulidou, Thanasis Naskos, Katia Lida Kermanidis, Vilelmini Sosoni, Hugo de Vos, Maria Stasimioti, Menno van Zaanen, Panayota Georgakopoulou, Valia Kordoni, Maja Popovic, Markus Egg, Antal Van den Bosch
Cross-Lingual Semantic Textual Similarity Machine Translation +1