Arne Mauser


2012

pdf bib
Deciphering Foreign Language by Combining Language Models and Context Vectors
Malte Nuhn | Arne Mauser | Hermann Ney
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Forced Derivations for Hierarchical Machine Translation
Stephan Peitz | Arne Mauser | Joern Wuebker | Hermann Ney
Proceedings of COLING 2012: Posters

2010

pdf bib
Training Phrase Translation Models with Leaving-One-Out
Joern Wuebker | Arne Mauser | Hermann Ney
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

pdf bib
Extending Statistical Machine Translation with Discriminative and Trigger-Based Lexicon Models
Arne Mauser | Saša Hasan | Hermann Ney
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

pdf bib
Automatic Evaluation Measures for Statistical Machine Translation System Optimization
Arne Mauser | Saša Hasan | Hermann Ney
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Evaluation of machine translation (MT) output is a challenging task. In most cases, there is no single correct translation. In the extreme case, two translations of the same input can have completely different words and sentence structure while still both being perfectly valid. Large projects and competitions for MT research raised the need for reliable and efficient evaluation of MT systems. For the funding side, the obvious motivation is to measure performance and progress of research. This often results in a specific measure or metric taken as primarily evaluation criterion. Do improvements in one measure really lead to improved MT performance? How does a gain in one evaluation metric affect other measures? This paper is going to answer these questions by a number of experiments.

2006

pdf bib
Training a Statistical Machine Translation System without GIZA++
Arne Mauser | Evgeny Matusov | Hermann Ney
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

The IBM Models (Brown et al., 1993) enjoy great popularity in the machine translation community because they offer high quality word alignments and a free implementation is available with the GIZA++ Toolkit (Och and Ney, 2003). Several methods have been developed to overcome the asymmetry of the alignment generated by the IBM Models. A remaining disadvantage, however, is the high model complexity. This paper describes a word alignment training procedure for statistical machine translation that uses a simple and clear statistical model, different from the IBM models. The main idea of the algorithm is to generate a symmetric and monotonic alignment between the target sentence and a permutation graph representing different reorderings of the words in the source sentence. The quality of the generated alignment is shown to be comparable to the standard GIZA++ training in an SMT setup.

2005

pdf bib
Translating with Non-contiguous Phrases
Michel Simard | Nicola Cancedda | Bruno Cavestro | Marc Dymetman | Eric Gaussier | Cyril Goutte | Kenji Yamada | Philippe Langlais | Arne Mauser
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing