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• W2144642230 abstract "Parallel data in the domain of interest is the key resource when training a statistical machine translation (SMT) system for a specific purpose. Since ad-hoc manual translation can represent a significant investment in time and money, a prior assesment of the amount of training data required to achieve a satisfactory accuracy level can be very useful. In this work, we show how to predict what the learning curve would look like if we were to manually translate increasing amounts of data.We consider two scenarios, 1) Monolingual samples in the source and target languages are available and 2) An additional small amount of parallel corpus is also available. We propose methods for predicting learning curves in both these scenarios." @default.
• W2144642230 created "2016-06-24" @default.
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• W2144642230 date "2012-07-08" @default.
• W2144642230 modified "2023-09-26" @default.
• W2144642230 title "Prediction of Learning Curves in Machine Translation" @default.
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