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This paper describes the system by FBK HLT-MT for cross-lingual semantic textual similarity measurement. Our approach is based on supervised regression with an ensemble decision tree. In order to assign a semantic similarity score to an input sentence pair, the model combines features collected by state-of-the-art methods in machine translation quality(More)
We address the problem of automatically cleaning a translation memory (TM) by identifying problematic translation units (TUs). In this context, we treat as “problematic TUs” those containing useless translations from the point of view of the user of a computer-assisted translation tool. We approach TM cleaning both as a supervised and as an unsupervised(More)
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