Evaluating Machine Translation for Emotion-loaded User Generated Content (TransEval4Emo-UGC)

Shenbin Qian, Constantin Orasan, Félix Do Carmo, Diptesh Kanojia


Abstract
This paper presents a dataset for evaluating the machine translation of emotion-loaded user generated content. It contains human-annotated quality evaluation data and post-edited reference translations. The dataset is available at our GitHub repository.
Anthology ID:
2024.eamt-2.22
Volume:
Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)
Month:
June
Year:
2024
Address:
Sheffield, UK
Editors:
Carolina Scarton, Charlotte Prescott, Chris Bayliss, Chris Oakley, Joanna Wright, Stuart Wrigley, Xingyi Song, Edward Gow-Smith, Mikel Forcada, Helena Moniz
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation (EAMT)
Note:
Pages:
43–44
Language:
URL:
https://aclanthology.org/2024.eamt-2.22
DOI:
Bibkey:
Cite (ACL):
Shenbin Qian, Constantin Orasan, Félix Do Carmo, and Diptesh Kanojia. 2024. Evaluating Machine Translation for Emotion-loaded User Generated Content (TransEval4Emo-UGC). In Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2), pages 43–44, Sheffield, UK. European Association for Machine Translation (EAMT).
Cite (Informal):
Evaluating Machine Translation for Emotion-loaded User Generated Content (TransEval4Emo-UGC) (Qian et al., EAMT 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.eamt-2.22.pdf