Ghent University Academic Bibliography

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ShAnEL-2 : a multilingual benchmarking dataset for short-answer language learning exercises

Jasper Degraeuwe (UGent) and Thomas Moerman (UGent)

(2026) Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026). In LREC proceedings p.6764-6771
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Shared task for cross-lingual classification of Corporate Social Responsibility (CSR) themes and topics

Yola Nayekoo, Sophia Katrenko, Veronique Hoste (UGent) , Aaron Maladry (UGent) and Els Lefever (UGent)

(2024) Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing. In LREC proceedings p.283-291
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GECO-MT : the Ghent Eye-tracking Corpus of Machine Translation

Toon Colman (UGent) , Margot Fonteyne (UGent) , Joke Daems (UGent) , Nicolas Dirix (UGent) and Lieve Macken (UGent)

(2022) LREC 2022 : THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION. In LREC Proceedings p.29-38
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LeConTra : a learner corpus of English-to-Dutch news translation

Bram Vanroy (UGent) and Lieve Macken (UGent)

(2022) LREC 2022 : THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION. In LREC Proceedings p.1807-1816
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Challenges with sign language datasets for sign language recognition and translation

Mirella De Sisto, Vincent Vandeghinste, Santiago Egea Gomez, Mathieu De Coster (UGent) , Dimitar Shterionov and Horacio Saggion

(2022) PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2022). In LREC Proceedings p.2478-2487

Academic Bibliography

ShAnEL-2 : a multilingual benchmarking dataset for short-answer language learning exercises

Shared task for cross-lingual classification of Corporate Social Responsibility (CSR) themes and topics

GECO-MT : the Ghent Eye-tracking Corpus of Machine Translation

LeConTra : a learner corpus of English-to-Dutch news translation

Challenges with sign language datasets for sign language recognition and translation

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