This paper considers a scenario which is slightly different from Statistical Machine Translation (SMT) in that we are given almost perfect knowledge about bilingual terminology, considering the situation when a Japanese patent is applied to or granted by the Japanese Patent Office (JPO). Technically, we incorporate bilingual terminology into Phrase-based SMT (PB-SMT) focusing on the statistical...

Machine Translation is one of the major oldest and the most active research area in Natural Language Processing. Currently, Statistical Machine Translation (SMT) dominates the Machine Translation research. Statistical Machine Translation is an approach to Machine Translation which uses models to learn translation patterns directly from data, and generalize them to translate a new unseen text. T...

In an ordinary syntactic parser, the input is a string, and the grammar ranges over strings. This paper explores generalizations of ordinary parsing algorithms that allow the input to consist of string tuples and/or the grammar to range over string tuples. Such algorithms can infer the synchronous structures hidden in parallel texts. It turns out that these generalized parsers can do most of th...

This paper describes our statistical machine translation system (CASIA) used in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2008. In this year's evaluation, we participated in challenge task for Chinese-English and English-Chinese, BTEC task for Chinese-English. Here, we mainly introduce the overview of our system, the primary modules, the key te...

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While statistical machine translation (SMT) has advanced significantly with better modeling techniques and much more training data, domain specific SMT has received much less attention and leaves much room for further improvements. In this work, we address domain issues and propose to use the combination of feature weights and language model adaptation, to distinguish multiple domains, which sh...

We propose the use of stacking, an ensemble learning technique, to the statistical machine translation (SMT) models. A diverse ensemble of weak learners is created using the same SMT engine (a hierarchical phrase-based system) by manipulating the training data and a strong model is created by combining the weak models on-the-fly. Experimental results on two language pairs and three different si...