Student: Senne Van Hoecke
Promotor(s): Orphée De Clercq
Student: Luca Desmet
Promotor(s): Sonia Vandepitte and Lieve Macken
Student: Liesbeth Allein
Promotor(s): Els Lefever
Student: Naomi Ackaert
Promotor(s): Sofie Labat and Véronique Hoste
Student: Lise Verstraete
Promotor(s): Lieve Macken and Bram Vanroy
Student: Rembert Hautekiet
Promotor(s): Lieve Macken
Student: Siel Debouver
Promotor(s): Orphée De Clercq and Cynthia Van Hee
Student: Antoine Vanrysselberghe
Promotor(s): Els Lefever
Student: Eileen Wemmer
Promotor(s): Sofie Labat and Roman Klinger
Student: Annaïs Airapetian
Promotor(s): Orphée De Clercq and Luna De Bruyne
Student: Charlotte Desplenter
Promotor(s): Orphée De Clercq
Student: Marie D'Hondt
Promotor(s): Orphée De Clercq
Student: Silke Bekaert
Promotor(s): Orphée De Clercq
Promotor(s): Els Lefever
Despite the great progress witnessed in recent years for various NLP tasks, lower-resourced languages often have difficulties keeping up because of data scarcity. A possible solution to this problem is the use of cross-lingual information, where knowledge or data from a rich-resourced language, like English, is used to improve the modeling in a low(er)-resourced target language, like Hindi or Dutch. The proposed research aims at constructing a manually labeled data set for the chosen target language, and to develop a cross-lingual Machine Learning approach and evaluate it on the downstream task of Sentiment Analysis (or hate speech detection).
Promotor(s): Lieve Macken
The main goal of this project is to examine whether and how Machine Translation (MT) can be used to support (language) teachers to adequately address linguistic diversity in the classroom and more specifically whether MT can be used as support tool for text comprehension.