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One of the major bottlenecks in the development of data-driven AI Systems is the cost of reliable human annotations. The recent advent of several crowdsourcing platforms such as Amazon's Mechanical Turk, allowing re-questers the access to affordable and rapid results of a global workforce, greatly facilitates the creation of massive training data. Most of(More)
For the CLEF 2004 ImageCLEF St Andrew's Collection task the Dublin City University group carried out three sets of experiments. We carried out standard cross-language information retrieval (CLIR) runs using topic translation using machine translation (MT), combination of this run with image matching results from the VIPER system, and a novel document(More)
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(More)
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(More)
We propose 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. We provide details of our method,(More)
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(More)
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.(More)
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