this article the problem of finding the word alignment of a bilingual sentence-aligned corpus by using language-independent statistical methods. There is a vast literature on this topic, and many different systems have been suggested to solve this problem. Our work follows and extends the methods introduced by Brown, Della Pietra, Della Pietra, and Mercer (1993) by using refined statistical models for the translation process. The basic idea of this approach is to develop a model of the translation process with the word alignment as a hidden variable of this process, to apply statistical estimation theory to compute the "optimal" model parameters, and to perform alignment search to compute the best word alignment

We discuss different strategies for smoothing the phrasetable in Statistical MT, and give results over a range of translation settings. We show that any type of smoothing is a better idea than the relativefrequency estimates that are often used. The best smoothing techniques yield consistent gains of approximately 1 % (absolute) according to the BLEU metric. 1

In this article, we describe an efficient beam search algorithm for statistical machine translation based on dynamic programming (DP). The search algorithm uses the translation model presented in Brown et al. (1993). Starting from a DP-based solution to the traveling-salesman problem, we present a novel technique to restrict the possible word reorderings between source and target language in order to achieve an efficient search algorithm. Word reordering restrictions especially useful for the translation direction German to English are presented. The restrictions are generalized, and a set of four parameters to control the word reordering is introduced, which then can easily be adopted to new translation directions. The beam search procedure has been successfully tested on the Verbmobil task (German to English, 8,000-word vocabulary) and on the Canadian Hansards task (French to English, 100,000-word vocabulary). For the medium-sized Verbmobil task, a sentence can be translated in a few seconds, only a small number of search errors occur, and there is no performance degradation as measured by the word error criterion used in this article. 1.

I describe two methods for incorporating information about the relative positions of bilingual word pairs into a Maximum Entropy/Minimum Divergence translation model. The better of the two achieves over 40% lower test corpus perplexity than an equivalent combination of a trigram language model and the classical IBM transla- tion model 2.

This paper addresses automatic transformation from spoken style texts to written style texts. Exact transcriptions and speech recognition results of live lectures include many spoken language expressions, and thus, are not suitable for documents and need to be edited. In this paper, we present a method of applying of the statistical approach used in machine translation to this post-processing task. Specifically, we implement the correction of colloquial expressions, the deletion of fillers, the insertion of periods, and the insertion of particles in an integrated manner. A preliminary evaluation confirms that the statistical transformation framework works well and we achieved high recall and precision rate of period and particle insertion.

I present empirical comparisons between a linear combination of stan- dard statistical language and translation models and an equivalent Maximum Entropy/Minimum Divergence (MEMD) model, using several different methods for automatic feature selection. The MEMD model significantly outperforms the standard model in test corpus perplexity, even though it has far fewer parameters.

This paper presents a decoder for statistical machine translation that can take advantage of the example-based machine translation framework. The decoder presented here is based on the greedy approach to the decoding problem, but the search is initiated from a similar translation extracted from a bilingual corpus. The experiments on multilingual translations showed that the proposed method was far superior to a word-by-word generation beam search algorithm. 1

This paper addresses automatic transformation from spoken style texts to written style texts. Exact transcriptions and speech recognition results of live lectures include many spoken language expressions, and thus, are not suitable for documents and need to be edited. In this paper, we present a method of applying of the statistical approach used in machine translation to this postprocessing task. Specifically, we implement the correction of colloquial expressions, the deletion of fillers, the insertion of periods, and the insertion of particles in an integrated manner. A preliminary evaluation confirms that the statistical transformation framework works well and we achieved high recall and precision rate of period and particle insertion.