Articles

This paper presents a new paradigm for translation from inflectionally rich lan- guages that was used in the University of Maryland statistical machine transla- tion system for the WMT07 Shared Task. The system is based on a hierarchical phrase-based decoder that has been aug- mented to translate ambiguous input given …...

This article describes a machine translation system based on an automatic post-editing strategy: initially translate the input text into the target-language using a rule-based MT system, then automatically post-edit the out- put using a statistical phrase-based system. An implementation of this approach based on the SYSTRAN and PORTAGE MT sys- …...

In this paper we describe the Interactive Sys- tems Laboratories (ISL) phrase-based ma- chine translation system used in the shared task ”Machine Translation for European Languages” of the ACL 2007 Workshop on Statistical Machine Translation. We present results for a system combination of the ISL syntax-augmented MT system and the …...

We describe the CMU-UKA Syntax Augmented Machine Translation system ‘SAMT’ used for the shared task “Machine Translation for European Lan- guages” at the ACL 2007 Workshop on Statistical Machine Translation. Following an overview of syn- tax augmented machine translation, we describe pa- rameters for components in our open-source SAMT toolkit …...

This paper describes the development of a statistical machine translation system based on the Moses decoder for the 2007 WMT shared tasks. Several different translation strategies were explored. We also use a sta- tistical language model that is based on a continuous representation of the words in the vocabulary. By …...

We present the PORTAGE statistical machine translation system which par- ticipated in the shared task of the ACL 2007 Second Workshop on Statistical Machine Translation. The focus of this description is on improvements which were incorporated into the system over the last year. These include adapted lan- guage models, phrase …...

We present results and experiences from our experiments with phrase-based statisti- cal machine translation using Moses. The paper is based on the idea of using an off- the-shelf parser to supply linguistic infor- mation to a factored translation model and compare the results of German–English translation to the shared task …...

Mixture modelling is a standard technique for density estimation, but its use in sta- tistical machine translation (SMT) has just started to be explored. One of the main advantages of this technique is its capabil- ity to learn specific probability distributions that better fit subsets of the training dataset. This …...

One main challenge of statistical machine trans- lation (SMT) is dealing with word order. The main idea of the statistical machine reordering (SMR) approach is to use the powerful tech- niques of SMT systems to generate a weighted reordering graph for SMT systems. This tech- nique supplies reordering constraints to …...

In this work we revise the application of discriminative learning to the problem of phrase selection in Statistical Machine Translation. Inspired by common tech- niques used in Word Sense Disambiguation, we train classifiers based on local context to predict possible phrase translations. Our work extends that of Vickrey et al. …...

We describe an architecture that allows to combine statistical machine translation (SMT) with rule-based machine translation (RBMT) in a multi-engine setup. We use a variant of standard SMT technology to align translations from one or more RBMT sys- tems with the source text. We incorporate phrases extracted from these alignments …...

We describe a mixture-model approach to adapting a Statistical Machine Translation System for new domains, using weights that depend on text distances to mixture compo- nents. We investigate a number of variants on this approach, including cross-domain versus dynamic adaptation; linear versus loglinear mixtures; language and transla- tion model adaptation; …...

This paper evaluates the translation quality of machine translation systems for 8 lan- guage pairs: translating French, German, Spanish, and Czech to English and back. We carried out an extensive human evalua- tion which allowed us not only to rank the different MT systems, but also to perform higher-level analysis …...

Most state-of-the-art statistical machine translation systems use log-linear models, which are defined in terms of hypothesis fea- tures and weights for those features. It is standard to tune the feature weights in or- der to maximize a translation quality met- ric, using held-out test sentences and their corresponding reference translations. …...

Attempts to estimate phrase translation probablities for statistical machine transla- tion using iteratively-trained models have repeatedly failed to produce translations as good as those obtained by estimating phrase translation probablities from surface statis- tics of bilingual word alignments as de- scribed by Koehn, et al. (2003). We pro- pose a …...

We present a method for evaluating the quality of Machine Translation (MT) output, using labelled dependencies produced by a Lexical-Functional Grammar (LFG) parser. Our dependency- based method, in contrast to most popular string-based evaluation metrics, does not unfairly penalize perfectly valid syntactic variations in the translation, and the addition of …...

This paper describes the 2007 Ngram-based sta- tistical machine translation system developed at the TALP Research Center of the UPC (Uni- versitat Polit`ecnica de Catalunya) in Barcelona. Emphasis is put on improvements and extensions of the previous years system, being highlighted and empirically compared. Mainly, these include a novel word …...

We introduce a novel evaluation scheme for the human evaluation of different machine translation systems. Our method is based on direct comparison of two sentences at a time by human judges. These binary judg- ments are then used to decide between all possible rankings of the systems. The ad- vantages …...

Statistical machine translation, as well as other areas of human language processing, have recently pushed toward the use of large scale n-gram language models. This paper presents efficient algorithmic and architec- tural solutions which have been tested within the Moses decoder, an open source toolkit for statistical machine translation. Exper- …...

In this paper, we present a Bayesian Learn- ing based method to train word dependent transition models for HMM based word alignment. We present word alignment re- sults on the Canadian Hansards corpus as compared to the conventional HMM and IBM model 4. We show that this method gives consistent …...