We report on a project to derive word translation relationships automatically from parallel corpora. Our effort is distinguished by the use of simpler, faster models than those used in previous high-accuracy approaches. Our methods achieve accuracy on singleword translations that seems comparable to any work previously reported, up to nearly 60% coverage of word types, and they perform particul...

Three new algorithms for 2D translation image registration to within a small fraction of a pixel that use nonlinear optimization and matrix-multiply discrete Fourier transforms are compared. These algorithms can achieve registration with an accuracy equivalent to that of the conventional fast Fourier transform upsampling approach in a small fraction of the computation time and with greatly redu...

In this paper, we investigate different methodologies of Arabic segmentation for statistical machine translation by comparing a rule-based segmenter to different statistically-based segmenters. We also present a new method for segmentation that serves the need for a real-time translation system without impairing the translation accuracy.

The translation of prepositions is often considered one of the more difficult tasks within the field of machine translation. We describe an experiment using transformationbased learning to induce rules to select the appropriate target language preposition from aligned bilingual data. Results show an accuracy of 84.9%, to be compared with a baseline of 75.5%, where the most frequent translation ...

This paper addresses a novel translation method based on Hidden Markov Model using template rules after learning them from the bilingual corpus. The method can enhance the translation accuracy and ensure a low complexity in comparing with the pervious template learning translation method and draws a new perspective for applying statistical machine learning on example based translations. domain.

This paper presents a strategy for detecting and using multi-word expressions in Statistical Machine Translation. Performance of the proposed strategy is evaluated in terms of alignment quality as well as translation accuracy. Evaluations are performed by using the Verbmobil corpus. Results from translation tasks from English-toSpanish and from Spanish-to-English are presented and discussed.

MOTIVATION Correct identification of the Translation Initiation Start (TIS) in cDNA sequences is an important issue for genome annotation. The aim of this work is to improve upon current methods and provide a performance guaranteed prediction. METHODS This is achieved by using two modules, one sensitive to the conserved motif and the other sensitive to the coding/non-coding potential around t...

In this paper, we describe a voting mechanism for accurate named entity (NE) translation in English–Chinese question answering (QA). This mechanism involves translations from three different sources: machine translation, online encyclopaedia, and web documents. The translation with the highest number of votes is selected. We evaluated this approach using test collection, topics and assessment r...

Boosting is a general method for improving the accuracy of a given learning algorithm under certain restrictions. In this work, AdaBoost, one of the most popular boosting algorithms, is adapted and applied to statistical machine translation. The appropriateness of this technique in this scenario is evaluated on a real translation task. Results from preliminary experiments confirm that statistic...

This paper proposes a method for learning a discriminative parser for machine translation reordering using only aligned parallel text. This is done by treating the parser’s derivation tree as a latent variable in a model that is trained to maximize reordering accuracy. We demonstrate that efficient large-margin training is possible by showing that two measures of reordering accuracy can be fact...