论文信息 - ParFDA for Fast Deployment of Accurate Statistical Machine Translation Systems, Benchmarks, and Statistics

ParFDA for Fast Deployment of Accurate Statistical Machine Translation Systems, Benchmarks, and Statistics

We build parallel FDA5 (ParFDA) Moses statistical machine translation (SMT) systems for all language pairs in the workshop on statistical machine translation (Bojar et al., 2015) (WMT15) translation task and obtain results close to the top with an average of 3.176 BLEU points difference using significantly less resources for building SMT systems. ParFDA is a parallel implementation of feature decay algorithms (FDA) developed for fast deploy

Andy Way | Qun Liu | Ergun Biçici

[1] Andreas Stolcke,et al. SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.

[2] Philipp Koehn,et al. Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[3] Andy Way,et al. Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems , 2014, WMT@ACL.

[4] Deniz Yuret,et al. Modeling Morphologically Rich Languages Using Split Words and Unstructured Dependencies , 2009, ACL.

[5] Hermann Ney,et al. A Systematic Comparison of Various Statistical Alignment Models , 2003, CL.

[6] Mehmet Ergun Biçici,et al. The Regression Model of Machine Translation , 2012 .

[7] Ergun Biçici,et al. Feature Decay Algorithms for Fast Deployment of Accurate Statistical Machine Translation Systems , 2013, WMT@ACL.

[8] Philipp Koehn,et al. Findings of the 2015 Workshop on Statistical Machine Translation , 2015, WMT@EMNLP.

[9] Deniz Yuret,et al. Optimizing Instance Selection for Statistical Machine Translation with Feature Decay Algorithms , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.