Abstract
We aim to shed light on the strengths and weaknesses of the newly introduced neural machine translation paradigm. To that end, we conduct a multifaceted evaluation in which we compare outputs produced by state-of-the-art neural machine translation and phrase-based machine translation systems for 9 language directions across a number of dimensions. Specifically, we measure the similarity of the outputs, their fluency and amount of reordering, the effect of sentence length and performance across different error categories. We find out that translations produced by neural machine translation systems are considerably different, more fluent and more accurate in terms of word order compared to those produced by phrase-based systems. Neural machine translation systems are also more accurate at producing inflected forms, but they perform poorly when translating very long sentences.- Anthology ID:
- E17-1100
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1063–1073
- Language:
- URL:
- https://aclanthology.org/E17-1100
- DOI:
- Cite (ACL):
- Antonio Toral and Víctor M. Sánchez-Cartagena. 2017. A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1063–1073, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions (Toral & Sánchez-Cartagena, EACL 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/E17-1100.pdf
- Code
- antot/neural_vs_-phrasebased_smt_eacl17
- Data
- WMT 2016