In this paper the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2011 is presented. We participated in the MT (English-French, Arabic-English, Chinese-English) and SLT

This paper describes the statistical ma-chine translation (SMT) systems devel-oped at RWTH Aachen University for the German→English translation task of the ACL 2014 Eighth Workshop on Statisti-cal Machine Translation (WMT 2014). Both hierarchical and phrase-based SMT systems are applied employing

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

RWTH participated in the shared transla-tion task of the Fourth Workshop of Sta-tistical Machine Translation (WMT 2009) with the German-English, French-English and Spanish-English pair in each transla-tion direction. The submissions were gen-erated using a phrase-based and a hierar

Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved. 1

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This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the translation task of the ACL 2013

This paper describes the statistical ma-chine translation (SMT) systems devel-oped at RWTH Aachen University for the German→English translation task of the ACL 2014 Eighth Workshop on Statisti-cal Machine Translation (WMT 2014). Both hierarchical and phrase-based SMT systems are applied employing

In this paper, we describe improved alignment models for statistical machine translation. The statistical translation approach uses two types of information: a translation model and a lan- guage model. The language model used is a bigram or general m-gram model. The translation model

RWTH participated in the System Combination task of the Fourth Workshop on Statistical Machine Translation (WMT 2009). Hypotheses from 9 German→English MT systems were combined into a consensus translation. This consensus translation scored 2.1 % better in BLEU and 2.3% better in TER (abs.) than