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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)
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