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Phrase Probabilities for Statistical MT using Belief Functions
In this work, we consider a specific part of statistical machine translation: feature estimation for the translation model. The classical way to estimate these features is based on relative frequencies. In this new approach, we propose to use the concept of belief masses to estimate the phrase translation probabilities. The Belief Function theory has proven to be suitable and adapted for dealing with uncertainties in many domains. Experiments have been performed to translate from English into French and from Arabic into English. They show that our approach performs, at least as well as and at times better than, the classical approach.