Abstract
This paper presents the NICT’s participation in the WMT18 shared parallel corpus filtering task. The organizers provided 1 billion words German-English corpus crawled from the web as part of the Paracrawl project. This corpus is too noisy to build an acceptable neural machine translation (NMT) system. Using the clean data of the WMT18 shared news translation task, we designed several features and trained a classifier to score each sentence pairs in the noisy data. Finally, we sampled 100 million and 10 million words and built corresponding NMT systems. Empirical results show that our NMT systems trained on sampled data achieve promising performance.- Anthology ID:
- W18-6489
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 963–967
- Language:
- URL:
- https://aclanthology.org/W18-6489
- DOI:
- 10.18653/v1/W18-6489
- Cite (ACL):
- Rui Wang, Benjamin Marie, Masao Utiyama, and Eiichiro Sumita. 2018. NICT’s Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 963–967, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- NICT’s Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task (Wang et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W18-6489.pdf