Minimum Bayes-risk System Combination

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1268–1277, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Minimum Bayes-risk System Combination Jes´us Gonz´alez-Rubio Instituto Tecnol´ogico de Inform´atica U. Polit`ecnica de Val`encia 46022 Valencia, Spain jegonzalez@iti.upv.es Alfons Juan Francisco Casacuberta D. de Sistemas Inform´aticos y Computaci´on U. Polit`ecnica de Val`encia 46022 Valencia, Spain {ajuan,fcn}@dsic.upv.es Abstract We present minimum Bayes-risk system com- bination, a method that integrates consen- sus decoding and system combination into a unified multi-system minimum Bayes-risk (MBR) technique. Unlike other MBR meth- ods that re-rank translations of a single SMT system, MBR system combination uses the MBR decision rule and a linear combina- tion of the component systems’ probability distributions to search for the minimum risk translation among all the finite-length strings over the output vocabulary. We introduce ex- pected BLEU, an approximation to the BLEU score that allows to efficiently apply MBR in these conditions. MBR system combination is a general method that is independent of spe- cific SMT models, enabling us to combine systems with heterogeneous structure. Exper- iments show that our approach bring sig- nificant improvements to single-system-based MBR decoding and achieves comparable re- sults to different state-of-the-art system com- bination methods. 1 Introduction Once statistical models are trained, a decoding ap- proach determines what translations are finally se- lected. Two parallel lines of research have shown consistent improvements over the max–derivation decoding objective, which selects the highest prob- ability derivation. Consensus decoding procedures select translations for a single system with a mini- mum Bayes risk (MBR) (Kumar and Byrne, 2004). System combination procedures, on the other hand, generate translations from the output of multiple component systems by combining the best frag- ments of these outputs (Frederking and Nirenburg, 1994). In this paper, we present minimum Bayes risk system combination, a technique that unifies these two approaches by learning a consensus trans- lation over multiple underlying component systems. MBR system combination operates directly on the outputs of the component models. We perform an MBR decoding using a linear combination of the component models’ probability distributions. In- stead of re-ranking the translations provided by the component systems, we search for the hypothesis with the minimum expected translation error among all the possible finite-length strings in the target lan- guage. By using a loss function based on BLEU (Pa- pineni et al., 2002), we avoid the hypothesis align- ment problem that is central to standard system com- bination approaches (Rosti et al., 2007). MBR sys- tem combination assumes only that each translation model can produce expectations of n-gram counts; the latent derivation structures of the component sys- tems can differ arbitrary. This flexibility allows us to combine a great variety of SMT systems. The key contributions of this paper are three: the usage of a linear combination of distributions within the MBR decoding, which allows multiple SMT models to be involved in, and makes the computa- tion of n-grams statistics to be more accurate; the decoding in an extended search space, …

Content truncated to 500 words