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We describe a systematic analysis on the effectiveness of features commonly exploited for the problem of predicting machine translation quality. Using a feature selection technique based on Gaussian P...
Annotating linguistic data is often a complex, time consuming and expensive endeavour. Even with strict annotation guidelines, human subjects often deviate in their analyses, each bringing different b...
Quality estimation models provide feedback on the quality of machine translated texts. They are usually trained on humanannotated datasets, which are very costly due to its task-specific nature. We in...
We describe QUEST, an open source framework for machine translation quality estimation. The framework allows the extraction of several quality indicators from source segments, their translations, exte...

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