Fabienne Braune

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We address the problem of unsupervised and language-pair independent alignment of symmetrical and asymmetrical parallel corpora. Asymmetrical parallel corpora contain a large proportion of 1-to-0/0-to-1 and 1-to-many/many-to-1 sentence correspondences. We have developed a novel approach which is fast and allows us to achieve high accuracy in terms of F1 for(More)
1Universität Stuttgart, 2Columbia University, 3University of Southern California Abstract We investigate formalisms for capturing the relation between semantic graphs and English strings. Semantic graph corpora have spurred recent interest in graph transduction formalisms, but it is not yet clear whether such formalisms are a good fit for natural language(More)
We present a new translation model integrating the shallow local multi bottomup tree transducer. We perform a largescale empirical evaluation of our obtained system, which demonstrates that we significantly beat a realistic tree-to-tree baseline on the WMT 2009 English→German translation task. As an additional contribution we make the developed software and(More)
This paper describes the joint submission of the QT21 and HimL projects for the English→Romanian translation task of the ACL 2016 First Conference on Machine Translation (WMT 2016). The submission is a system combination which combines twelve different statistical machine translation systems provided by the different groups (RWTH Aachen University, LMU(More)
Current state-of-the-art statistical machine translation (SMT) relies on simple feature functionswhichmake independence assumptions at the level of phrases or hierarchical rules. However, it is well-known that discriminative models can benefit from rich features extracted from the source sentence context outside of the applied phrase or hierarchical rule,(More)
In syntax-based machine translation, rule selection is the task of choosing the correct target side of a translation rule among rules with the same source side. We define a discriminative rule selection model for systems that have syntactic annotation on the target language side (stringto-tree). This is a new and clean way to integrate soft source syntactic(More)
This paper describes the LMU Munich English→German machine translation systems. We participated with neural translation engines in the WMT17 shared task on machine translation of news, as well as in the biomedical translation task. LMU Munich’s systems deliver competitive machine translation quality on both news articles and health information texts.