Previous work on marker-based EBMT [Gough & Way, 2003, Way & Gough, 2004] suffered from problems such as data-sparseness and disparity between the training and test data. We have developed a largescale robust EBMT system. In a comparison with the systems listed in [Somers, 2003], ours is the third largest EBMT system and certainly the largest English-French EBMT system. Previous work used the on-line MT system Logomedia to translate source language material as a means of populating the system’s database where bitexts were unavailable. We derive our sententially aligned strings from a Sun Translation Memory (TM) and limit the integration of Logomedia to the derivation of our word-level lexicon. We also use Logomedia to provide a baseline comparison for our system and observe that we outperform Logomedia and previous marker-based EBMT systems in a number of tests. 1

Example-Based Machine Translation (EBMT) systems have typically operated on individual sentences without taking into account prior context. By adding a simple reweighting of retrieved fragments of training examples on the basis of whether the previous translation retrieved any fragments from examples within a small window of the current instance, translation performance is improved. A further improvement is seen by performing a similar reweighting when another fragment of the current input sentence was retrieved from the same training example. Together, a simple, straightforward implementation of these two factors results in an improvement on the order of 1.0--1.6% in the BLEU metric across multiple data sets in multiple languages.

for the Language Pair Italian-English This paper reports on an experiment investigating how effective free online machine translation (MT) is in helping Internet users to access the contents of websites written only in languages they do not know. This study explores the extent to which using Internet-based MT tools affects the confidence of web-surfers in the reliability of the information they find on websites available only in languages unfamiliar to them. The results of a case study for the language pair Italian-English involving 101 participants show that the chances of identifying correctly basic information (i.e. understanding the nature of websites and finding contact telephone numbers from their web-pages) are consistently enhanced to varying degrees (up to nearly 20%) by translating online content into a familiar language. In addition, confidence ratings given by users to the reliability and accuracy of the information they find are significantly higher (with increases between 5 and 11%) when they translate websites into their preferred language with free online MT services.

In this paper, we report our experiments in combining two EBMT systems that rely on generalized templates, Marclator and CMU-EBMT, on an English–German translation task. Our goal was to see whether a statistically significant improvement could be achieved over the individual performances of these two systems. We observed that this was not the case. However, our system consistently outperformed a lexical EBMT baseline system. 1

Abstract not found