Ergun Biçici, Qun Liu, and Andy Way. Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems. In Proceedings of the Ninth Workshop on Statistical Machine Translation, Baltimore, USA, June 2014. Association for Computational Linguistics. [PDF ] Keyword(s): Machine Translation, Machine Learning, Language Modeling. [Abstract][bibtex-entry]
We use parallel FDA5, an efficiently parameterized and optimized parallel implementation of feature decay algorithms for fast deployment of accurate statistical machine translation systems, taking only about half a day for each translation direction. We build Parallel FDA5 Moses SMT systems for all language pairs in the WMT14 translation task and obtain SMT performance close to the top Moses systems with an average of $3.49$ BLEU points difference using significantly less resources for training and development.
Researcher in Computer Science and Engineering
PhD in Computer Engineering from
Department of Computer Engineering, Koç University.
June 23, 2014
Monolingual and Bilingual Text Quality Judgments with Translation Performance Prediction
We won funding from SFI on "Monolingual and Bilingual Text Quality Judgments with Translation Performance Prediction" where we target solutions in text analytics, quality, and similarity with translation performance prediction technology. You are welcome to check out the project's website and read the related CNGL news article.
Subscribe to:
Posts (Atom)