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Summary: Improving Alignments for Better Confusion Networks
for Combining Machine Translation Systems
Necip Fazil Ayan and Jing Zheng and Wen Wang
SRI International
Speech Technology and Research Laboratory (STAR)
333 Ravenswood Avenue
Menlo Park, CA 94025
{nfa,zj,wwang}@speech.sri.com
Abstract
The state-of-the-art system combination
method for machine translation (MT) is
the word-based combination using confu-
sion networks. One of the crucial steps in
confusion network decoding is the align-
ment of different hypotheses to each other
when building a network. In this paper, we
present new methods to improve alignment
of hypotheses using word synonyms and a
two-pass alignment strategy. We demon-
strate that combination with the new align-
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