Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based

Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training

We collected a corpus of parallel text in 11 languages from the proceedings of the European Parliament, which are published on the web 1. This corpus has found widespread use in the NLP community. Here, we focus on its acquisition and its application as training data for statistical machine

We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder

A phrase-based statistical machine translation approach — the alignment template approach — is described. This translation approach allows for general many-to-many relations between words. Thereby, the context of words is taken into account in the translation model, and local changes in word order

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We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source -channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language

We present a statistical phrase-based translation model that uses hierarchical phrases— phrases that contain subphrases. The model is formally a synchronous context-free grammar but is learned from a bitext without any syntactic information. Thus it can be seen as a shift to the formal machinery

In this paper, we describe improved alignment modelsforstatisticalmachinetranslation. The statisticaltranslationapproachusestwotypes of information: a translationmodel and a language model. Thelanguagemodelusedisa bigramorgeneral m-gram model. The translation model is decomposed into a lexical and an alignment model. We describetwodierentap-proachesforstatisticaltranslationandpresent experimental results. The first approach is basedondependenciesbetweensinglewords, thesecondapproachexplicitlytakesshallow phrasestructuresintoaccount, using two different alignment levels: a phraselevelalignment betweenphrasesandawordlevelalignment between single words. Wepresentresultsus-ingtheVerbmobiltask (German-English, 6000-word vocabulary) whichisalimited-domain spoken-language task. Theexperimentaltests wereperformedonboththetext transcription and thespeechrecognizeroutput.

We describe a method for incorporating syntactic information in statistical machine translation systems. The first step of the method is to parse the source language string that is being translated. The second step is to apply a series of transformations to the parse tree, effectively reordering