Abstract
Parallel corpora are often not as parallel as one might assume: non-literal translations and noisy translations abound, even in curated corpora routinely used for training and evaluation. We use a cross-lingual textual entailment system to distinguish sentence pairs that are parallel in meaning from those that are not, and show that filtering out divergent examples from training improves translation quality.- Anthology ID:
- W17-3209
- Volume:
- Proceedings of the First Workshop on Neural Machine Translation
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver
- Editors:
- Thang Luong, Alexandra Birch, Graham Neubig, Andrew Finch
- Venue:
- NGT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 69–79
- Language:
- URL:
- https://aclanthology.org/W17-3209
- DOI:
- 10.18653/v1/W17-3209
- Cite (ACL):
- Marine Carpuat, Yogarshi Vyas, and Xing Niu. 2017. Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation. In Proceedings of the First Workshop on Neural Machine Translation, pages 69–79, Vancouver. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation (Carpuat et al., NGT 2017)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W17-3209.pdf
- Data
- OpenSubtitles