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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2024.eamt-2.22.pdf