Arun, Abhishek

Haddow, Barry

Koehn, Philipp

We present a unified approach to performing minimum risk training and minimum Bayes risk (MBR) decoding with BLEU in a phrase-based model. Key to our approach is the use of a Gibbs sampler that allows us to explore the entire probability distribution and maintain a strict probabilistic formulation across the pipeline. We also describe a new sampling algorithm called corpus sampling which allows us at training time to use BLEU instead of an approximation thereof. Our approach is theoretically sound and gives better (up to +0.6%BLEU) and more stable results than the standard MERT optimization algorithm. By comparing our approach to lattice MBR, we are also able to gain crucial insights about both methods

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A Unified Approach to Minimum Risk Training and Decoding

https://www.pure.ed.ac.uk/ws/files/19319625/Arun_Haddow_ET_AL_2010_A_Unified_Approach_to_Minimum_Risk_Training_and_Decoding.pdf

A Unified Approach to Minimum Risk Training and Decoding

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