TAILIEUCHUNG - Báo cáo khoa học: "A Comparative Study of Hypothesis Alignment and its Improvement for Machine Translation System Combination"

Recently confusion network decoding shows the best performance in combining outputs from multiple machine translation (MT) systems. However, overcoming different word orders presented in multiple MT systems during hypothesis alignment still remains the biggest challenge to confusion network-based MT system combination. In this paper, we compare four commonly used word alignment methods, namely GIZA++, TER, CLA and IHMM, for hypothesis alignment. Then we propose a method to build the confusion network from intersection word alignment, which utilizes both direct and inverse word alignment between the backbone and hypothesis to improve the reliability of hypothesis alignment. . | A Comparative Study of Hypothesis Alignment and its Improvement for Machine Translation System Combination Boxing Chen Min Zhang Haizhou Li and Aiti Aw Institute for Infocomm Research 1 Fusionopolis Way 138632 Singapore bxchen mzhang hli aaiti @ Abstract Recently confusion network decoding shows the best performance in combining outputs from multiple machine translation MT systems. However overcoming different word orders presented in multiple MT systems during hypothesis alignment still remains the biggest challenge to confusion network-based MT system combination. In this paper we compare four commonly used word alignment methods namely GiZA ter cLa and IHMM for hypothesis alignment. Then we propose a method to build the confusion network from intersection word alignment which utilizes both direct and inverse word alignment between the backbone and hypothesis to improve the reliability of hypothesis alignment. Experimental results demonstrate that the intersection word alignment yields consistent performance improvement for all four word alignment methods on both Chi-nese-to-English spoken and written language tasks. 1 Introduction Machine translation MT system combination technique leverages on multiple MT systems to achieve better performance by combining their outputs. Confusion network based system combination for machine translation has shown promising advantage compared with other techniques based system combination such as sentence level hypothesis selection by voting and source sentence re-decoding using the phrases or translation models that are learned from the source sentences and target hypotheses pairs Rosti et al. 2007a Huang and Papineni 2007 . In general the confusion network based system combination method for MT consists of four steps 1 Backbone selection to select a backbone also called skeleton from all hypotheses. The backbone defines the word orders of the fi nal translation. 2 Hypothesis alignment to build word-alignment .

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• Machine Translation System Combination
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