Abstract
Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese–English news translation task. We empirically test this claim with alternative evaluation protocols, contrasting the evaluation of single sentences and entire documents. In a pairwise ranking experiment, human raters assessing adequacy and fluency show a stronger preference for human over machine translation when evaluating documents as compared to isolated sentences. Our findings emphasise the need to shift towards document-level evaluation as machine translation improves to the degree that errors which are hard or impossible to spot at the sentence-level become decisive in discriminating quality of different translation outputs.- Anthology ID:
- D18-1512
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4791–4796
- Language:
- URL:
- https://aclanthology.org/D18-1512
- DOI:
- 10.18653/v1/D18-1512
- Cite (ACL):
- Samuel Läubli, Rico Sennrich, and Martin Volk. 2018. Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4791–4796, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation (Läubli et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D18-1512.pdf
- Code
- laeubli/parity