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Confidence-Weighted Learning of Factored Discriminative Language Models

Viet Ha-Thuc, Nicola Cancedda
Language modeling is a key component in most statistical machine translation systems, where it plays a crucial role in promoting out put fluency. Since they rely on word sur face forms only, mainstream language mod els are unable to benefit from available lin guistic knowledge sources. Moreover, they tend to suffer from poor estimates for rare fea tures. In this disclosure we propose an ap proach to overcome these two limitations. For the first one, we use factored features that can flexibly capture linguistic regularities. To overcome the second, we adopt confidence weighted learning, a form of discriminative online learning that can better take advantage of a heavy tail of rare features. Finally, we ex tend the confidence-weighted learning to deal with noise in training data, the most common case with language modeling.
ACL/HLT 2011, June 19-24, 2011, Portland, Oregon, USA.