TAILIEUCHUNG - Báo cáo khoa học: "Moses: Open Source Toolkit for Statistical Machine Translation"

We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks. | Moses Open Source Toolkit for Statistical Machine Translation Philipp Koehn Hieu Hoang Alexandra Birch Chris Callison-Burch University of Edinburgh1 Richard Zens RWTH Aachen4 Marcello Federico Nicola Bertoldi ITC-irst2 Chris Dyer University of Maryland5 Brooke Cowan Wade Shen Christine Moran MIT3 Ondrej Bojar Charles University6 Alexandra Constantin Evan Herbst Williams College7 Cornell8 1 pkoehn@ @ callison-burch@. 2 federico bertoldi @. 3 brooke@ swade@ weezer@. 4 zens@. 5 redpony@. 6 bojar@. 7 07aec_2@. 8 evh4@ Abstract We describe an open-source toolkit for statistical machine translation whose novel contributions are a support for linguistically motivated factors b confusion network decoding and c efficient data formats for translation models and language models. In addition to the SMT decoder the toolkit also includes a wide variety of tools for training tuning and applying the system to many translation tasks. 1 Motivation Phrase-based statistical machine translation Koehn et al. 2003 has emerged as the dominant paradigm in machine translation research. However until now most work in this field has been carried out on proprietary and in-house research systems. This lack of openness has created a high barrier to entry for researchers as many of the components required have had to be duplicated. This has also hindered effective comparisons of the different elements of the systems. By providing a free and complete toolkit we hope that this will stimulate the development of the field. For this system to be adopted by the community it must demonstrate performance that is comparable to the best available systems. Moses has 177 shown that it achieves results comparable to the most competitive and widely used statistical machine translation systems in translation quality and run-time Shen et al. 2006

• Báo cáo khoa học
• Open Source Toolkit
• Statistical Machine Translation
• Philipp Koehn Hieu Hoang Alexandra Birch Chris Callison Burch
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• An Open Source Toolkit
• Phrase based
• Syntax based Machine Translation
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Tree Forest Based Statistical Machine Translation
• Xianchao Wu
• Demonstration of Joshua
• Parsing based Machine Translation
• kỷ năng lập trình
• lập trình ứng dụng
• lập trình windows
• thủ thuật máy tính
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• bí quyết lập trình