Mirela-Stefania Duma

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We present in this paper the participation of the University of Hamburg in the Biomedical Translation Task of the Second Conference on Machine Translation (WMT 2017). Our contribution lies in adopting a new direction for performing data selection for Machine Translation via Paragraph Vector and a Feed Forward Neural Network Classifier. Continuous(More)
This paper describes our unsupervised knowledge-free approach to the SemEval2017 Task 1 Competition. The proposed method makes use of Paragraph Vector for assessing the semantic similarity between pairs of sentences. We experimented with various dimensions of the vector and three state-of-the-art similarity metrics. Given a cross-lingual task, we trained(More)
Large amounts of bilingual corpora are used in the training process of statistical machine translation systems. Usually a general domain is used as the training corpus. When the system is tested using data from the same domain, the obtained results are satisfactory, but if the test set belongs to a different domain, the translation quality decreases. This(More)
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