#9788Experimental results show that our approach improves domain-specific word alignment in terms of both precision and recall, achieving a relative error rate reduction of 6.56% as compared with the state-of-the-art technologies.Syntax-based statistical machine translation ( MT ) aims at applying statistical models to structured data.
tech,20-5-P05-1067,ak
</term>
, which can also be viewed as a
<term>
stochastic tree-to-tree transducer
</term>
. We introduce a
<term>
polynomial
#9874Second, we describe the graphical model for the machine translation task, which can also be viewed as astochastic tree-to-tree transducer.
measure(ment),17-8-P05-1067,ak
the
<term>
IBM models
</term>
in both
<term>
translation speed and quality
</term>
. In this paper , we present a novel
#9924The result shows that our system outperforms the baseline system based on the IBM models in bothtranslation speed and quality.
tech,8-8-P05-1067,ak
shows that our system outperforms the
<term>
baseline system
</term>
based on the
<term>
IBM models
</term>
#9915The result shows that our system outperforms thebaseline system based on the IBM models in both translation speed and quality.
tech,3-6-P05-1067,ak
transducer
</term>
. We introduce a
<term>
polynomial time decoding algorithm
</term>
for the
<term>
model
</term>
. We evaluate
#9881We introduce apolynomial time decoding algorithm for the model.
lr,9-4-P05-1067,ak
introduce our approach to inducing such a
<term>
grammar
</term>
from
<term>
parallel corpora
</term>
#9849We first introduce our approach to inducing such agrammar from parallel corpora.