The RWTH Aachen machine translation system for IWSLT 2011
Joern Wuebker, Matthias Huck, Saab Mansour, Markus Freitag, Minwei Feng, Stephan Peitz, Christoph Schmidt, Hermann Ney
Abstract
In this paper the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2011 is presented. We participated in the MT (English-French, Arabic-English, ChineseEnglish) and SLT (English-French) tracks. Both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated, including domain adaptation via monolingual and bilingual data selection, phrase training, different lexical smoothing methods, additional reordering models for the hierarchical system, various Arabic and Chinese segmentation methods, punctuation prediction for speech recognition output, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems.- Anthology ID:
- 2011.iwslt-evaluation.14
- Volume:
- Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign
- Month:
- December 8-9
- Year:
- 2011
- Address:
- San Francisco, California
- Editors:
- Marcello Federico, Mei-Yuh Hwang, Margit Rödder, Sebastian Stüker
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- Note:
- Pages:
- 106–113
- Language:
- URL:
- https://aclanthology.org/2011.iwslt-evaluation.14
- DOI:
- Cite (ACL):
- Joern Wuebker, Matthias Huck, Saab Mansour, Markus Freitag, Minwei Feng, Stephan Peitz, Christoph Schmidt, and Hermann Ney. 2011. The RWTH Aachen machine translation system for IWSLT 2011. In Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign, pages 106–113, San Francisco, California.
- Cite (Informal):
- The RWTH Aachen machine translation system for IWSLT 2011 (Wuebker et al., IWSLT 2011)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2011.iwslt-evaluation.14.pdf