Abstract

The ever-increasing amount of parallel data opens a rich resource to multilingual natural language processing, enabling models to work on various translational aspects like detailed human annotations, syntax and semantics. With efficient statistical models, many cross-language applications have seen significant progresses in recent years, such as statistical machine trans-lation, speech-to-speech translation, cross-lingual information retrieval and bilingual lexicog-raphy. However, the current state-of-the-art statistical translation models rely heavily on the word-level mixture models — a bottleneck, which fails to represent the rich varieties and depen-dencies in translations. In contrast to word-based translations, phrase-based models are more robust in capturing various translation phenomena than the word-level (e.g., local word reordering), and less susceptive to the errors from preprocessing such as word segmentations and tok-enizations. Leveraging phrase level knowledge in translation models is challenging yet reward-ing: it also brings significant improvements on translation qualities. Above the phrase-level are

Citations