In (Mellebeek et al., 2005), we proposed the design, implementation and evaluation of a novel and modular approach to boost the translation performance of existing, wide-coverage, freely available machine translation systems, based on reliable and fast automatic decomposition of the translation input and corresponding composition of translation output. Despite showing some initial promise, our method did not improve on the baseline Logomedia 1 and Systran 2 MT systems. In this paper, we improve on the algorithm presented in (Mellebeek et al., 2005), and on the same test data, show increased scores for a range of automatic evaluation metrics. Our algorithm now outperforms Logomedia, obtains similar results to SDL 3 and falls tantalisingly short of the performance achieved by Systran. 1

We present a method for improving statistical machine translation performance by using linguistically motivated syntactic information. Our algorithm recursively decomposes source language sentences into syntactically simpler and shorter chunks, and recomposes their translation to form target language sentences. This improves both the word order and lexical selection of the translation. We report statistically significant relative improvements of 3.3 % BLEU score in an experiment (English→Spanish) carried out on an 800-sentence test set extracted from the Europarl corpus. 1

TransBooster is a wrapper technology designed to improve the performance of wide-coverage machine translation systems. Using linguistically motivated syntactic information, it automatically decomposes source language sentences into shorter and syntactically simpler chunks, and recomposes their translation to form target language sentences. This generally improves both the word order and lexical selection of the translation. To date, TransBooster has been successfully applied to rule-based MT, statistical MT, and multi-engine MT. This paper presents the application of TransBooster to Example-Based Machine Translation. In an experiment conducted on test sets extracted from Europarl and the Penn II Treebank we show that our method can raise the BLEU score up to 3.8 % relative to the EBMT baseline. We also conduct a manual evaluation, showing that TransBooster-enhanced EBMT produces a better output in terms of fluency than the baseline EBMT in 55 % of the cases and in terms of accuracy in 53 % of the cases.

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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.