Abstract
This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign International Workshop on Spoken Language Translation (IWSLT) 2014. We participated in both the MT and SLT tracks for the English→French and German→English language pairs and applied the identical training pipeline and models on both language pairs. Our state-of-the-art phrase-based baseline systems are augmented with maximum expected BLEU training for phrasal, lexical and reordering models. Further, we apply rescoring with novel recurrent neural language and translation models. The same systems are used for the SLT track, where we additionally perform punctuation prediction on the automatic transcriptions employing hierarchical phrase-based translation. We are able to improve RWTH’s 2013 evaluation systems by 1.7-1.8% BLEU absolute.- Anthology ID:
- 2014.iwslt-evaluation.22
- Volume:
- Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign
- Month:
- December 4-5
- Year:
- 2014
- Address:
- Lake Tahoe, California
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- Note:
- Pages:
- 150–154
- Language:
- URL:
- https://aclanthology.org/2014.iwslt-evaluation.22
- DOI:
- Cite (ACL):
- Joern Wuebker, Stephan Peitz, Andreas Guta, and Hermann Ney. 2014. The RWTH Aachen machine translation systems for IWSLT 2014. In Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign, pages 150–154, Lake Tahoe, California.
- Cite (Informal):
- The RWTH Aachen machine translation systems for IWSLT 2014 (Wuebker et al., IWSLT 2014)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2014.iwslt-evaluation.22.pdf