The paper introduces Moses, an open-source toolkit for statistical machine translation (SMT) that supports linguistically motivated factors, confusion network decoding, and efficient data formats for translation and language models. The toolkit aims to reduce the barrier to entry for researchers by providing a complete set of tools for training, tuning, and applying the system to various translation tasks. Key features include: 1. **Linguistically Motivated Factors**: Enhances translation quality by integrating morphological, syntactic, and semantic information. 2. **Confusion Network Decoding**: Allows for the translation of ambiguous input, improving integration with speech recognition and machine translation. 3. **Efficient Data Structures**: Utilizes prefix trees and on-demand loading to manage large datasets efficiently, reducing memory requirements. The toolkit is designed to be accessible, easy to maintain, flexible, and portable, making it suitable for distributed team development. It includes tools for preprocessing, training, tuning, and evaluating models, and integrates with external tools like GIZA++ and SRILM. Moses has been widely adopted, with over 1000 downloads as of March 1, 2007, and has been featured in the Johns Hopkins University Workshop on Machine Translation. The paper concludes by highlighting the potential benefits of these features and the need for further research in this area.The paper introduces Moses, an open-source toolkit for statistical machine translation (SMT) that supports linguistically motivated factors, confusion network decoding, and efficient data formats for translation and language models. The toolkit aims to reduce the barrier to entry for researchers by providing a complete set of tools for training, tuning, and applying the system to various translation tasks. Key features include: 1. **Linguistically Motivated Factors**: Enhances translation quality by integrating morphological, syntactic, and semantic information. 2. **Confusion Network Decoding**: Allows for the translation of ambiguous input, improving integration with speech recognition and machine translation. 3. **Efficient Data Structures**: Utilizes prefix trees and on-demand loading to manage large datasets efficiently, reducing memory requirements. The toolkit is designed to be accessible, easy to maintain, flexible, and portable, making it suitable for distributed team development. It includes tools for preprocessing, training, tuning, and evaluating models, and integrates with external tools like GIZA++ and SRILM. Moses has been widely adopted, with over 1000 downloads as of March 1, 2007, and has been featured in the Johns Hopkins University Workshop on Machine Translation. The paper concludes by highlighting the potential benefits of these features and the need for further research in this area.