Bart Mellebeek


2010

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Opinion Mining of Spanish Customer Comments with Non-Expert Annotations on Mechanical Turk
Bart Mellebeek | Francesc Benavent | Jens Grivolla | Joan Codina | Marta R. Costa-jussà | Rafael Banchs
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

2006

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Multi-Engine Machine Translation by Recursive Sentence Decomposition
Bart Mellebeek | Karolina Owczarzak | Josef Van Genabith | Andy Way
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers

In this paper, we present a novel approach to combine the outputs of multiple MT engines into a consensus translation. In contrast to previous Multi-Engine Machine Translation (MEMT) techniques, we do not rely on word alignments of output hypotheses, but prepare the input sentence for multi-engine processing. We do this by using a recursive decomposition algorithm that produces simple chunks as input to the MT engines. A consensus translation is produced by combining the best chunk translations, selected through majority voting, a trigram language model score and a confidence score assigned to each MT engine. We report statistically significant relative improvements of up to 9% BLEU score in experiments (English→Spanish) carried out on an 800-sentence test set extracted from the Penn-II Treebank.

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Wrapper Syntax for Example-based Machine Translation
Karolina Owczarzak | Bart Mellebeek | Declan Groves | Josef Van Genabith | Andy Way
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers

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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A Syntactic Skeleton for Statistical Machine Translation
Bart Mellebeek | Karolina Owczarzak | Declan Groves | Josef Van Genabith | Andy Way
Proceedings of the 11th Annual conference of the European Association for Machine Translation

2005

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TransBooster: boosting the performance of wide-coverage machine translation systems
Bart Mellebeek | Anna Khasin | Josef Van Genabith | Andy Way
Proceedings of the 10th EAMT Conference: Practical applications of machine translation

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Improving Online Machine Translation Systems
Bart Mellebeek | Anna Khasin | Karolina Owczarzak | Josef Van Genabith | Andy Way
Proceedings of Machine Translation Summit X: Papers

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 Logomedia1 and Systran2 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 SDL3 and falls tantalisingly short of the performance achieved by Systran.