NDLI: A Three-Pass System Combination Framework by Combining Multiple Hypothesis Alignment Methods

Content Provider	IEEE Xplore Digital Library
Author	Jinhua Du Way, A.
Copyright Year	2009
Abstract	So far, many effective hypothesis alignment metrics have been proposed and applied to the system combination, such as TER, HMM, ITER and IHMM. In addition, the Minimum Bayes-risk (MBR) decoding and the confusion network (CN) have become the state-of-the-art techniques in system combination. In this paper, we present a three-pass system combination strategy that can combine hypothesis alignment results derived from different alignment metrics to generate a better translation. Firstly the different alignment metrics are carried out to align the backbone and hypotheses, and the individual CN is built corresponding to each alignment results; then we construct a super network by merging the multiple metric-based CN and generate a consensus output. Finally a modified consensus network MBR (ConMBR) approach is employed to search a best translation. Our proposed strategy outperforms the best single CN as well as the best single system in our experiments on NIST Chinese-to-English test set.
Starting Page	172
Ending Page	176
File Size	287400
Page Count	5
File Format	PDF
ISBN	9780769539041
DOI	10.1109/IALP.2009.44
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-07
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	System testing Costs Error analysis Spine Merging Hidden Markov models System combination NIST Three-pass Super network Hypothesis alignment Decoding
Content Type	Text
Resource Type	Article

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