In this paper, we describe a Dynamic Programming (DP) based search algorithm for statistical translation and present experimental results. The statistical translation uses two sources of information: a translation model and a language model. The language model used is a standard bigram model. For the translation lnodel, the alignment probabilities are made dependent on the differences in the alignment positions rather than on the absolute positions. Thus, the approach amounts to a first-order Hidden Markov model (HMM) as they are used successfully in speech recognition for the time alignment problem. Under the assumption that the alignment is monotone with respect to the word order in both languages, an efficient search strategy for translation can be formulated. The details of the search algorithm are described. Experiments on the EuTrans corpus produced a word error rate of 5.1(/~.. • the search procedure, i.e. an algorithm to perform the argmax operation in an efficient way. • transformation steps for both the source and the target languages in order to improve the translation process. The transformations are very much dependent on the language pair and the specific translation task and are therefore discussed in the context of the task description. We have to keep in mind that in the search procedure both the language and the translation model are applied after the text transformation steps. However, to keep the notation simple we will not make this explicit distinction in the subsequent exposition. The overall architecture of the statistical translation approach is summarized in Figure 1 .
REF proposed a DP-based monotone search algorithm for SMT.
A DP-Based Search Using Monotone Alignments In Statistical Translation
Annual Meeting Of The Association For Computational Linguistics
Computer Science