Symmetric word alignments for statistical machine translation
Abstract
In this paper, we address the word
alignment problem for statistical machine
translation. We aim at creating a symmetric
word alignment allowing for reliable
one-to-many and many-to-one word
relationships. We perform the iterative
alignment training in the source-to-target
and the target-to-source direction with
the well-known IBM and HMM alignment
models. Using these models, we robustly
estimate the local costs of aligning a source
word and a target word in each sentence
pair. Then, we use efficient graph algorithms
to determine the symmetric alignment
with minimal total costs (i. e. maximal
alignment probability). We evaluate
the automatic alignments created in
this way on the German–English Verbmobil
task and the French–English Canadian
Hansards task. We show statistically
significant improvements of the alignment
quality compared to the best results reported
so far. On the Verbmobil task,
we achieve an improvement of more than
1% absolute over the baseline error rate of
4.7%.