In this paper we describe the components of our statistical machine translation system. This system

In this paper we describe a decoder for statistical machine translation which allows controlled reordering of the words generated in the target language. After a general discussion of the structure of a decoder a particular implementation is discussed which allows for word-toword and phrase-to-phrase translation. Word reordering is used to improve the translation quality. We analyze the effect of the length of this reordering window on the search space and the translation quality. Results for Chinese-to-English and Arabic-to-English translation tasks are presented. 1.

In this paper we explore a technique for adding longer phrase translation pairs to a Statistical Machine Translation (SMT) system. New phrases are generated by merging existing phrase-level alignments that have overlapping words on both the source and target sides. The effect on translation quality is reported for an Arabic-English system in the news domain.

In this paper we propose a phrase-based translation system. In the system, we use phrase translation model instead of word-based model. An improved method of computing phrase translation probability is studied. We translate numeral phrases first by using a standard templates depository. We develop a phrase-based decoder that employs a beam search algorithm. To make the result more reasonable, we apply those words with fertility probability of zero. We improve the previously proposed tracing back algorithm to get the best path. Some experiments concerned are presented. 1