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.
We report on recent improvements in our English/Iraqi Arabic speech-to-speech translation system. User interface improvements include a novel parallel approach to user confirmation which makes confirmation cost-free in terms of dialog duration. Automatic speech recognition improvements include the incorporation of state-of-the-art techniques in feature transformation and discriminative training. Machine translation improvements include a novel combination of multiple alignments derived from various pre-processing techniques, such as Arabic segmentation and English word compounding, higher order N-grams for target language model, and use of context in form of semantic classes and Part-of-Speech tags.
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