We use a
<term>
maximum likelihood criterion
</term>
to train a
<term>
log-linear block bigram model
</term>
which uses
<term>
real-valued features
</term>
( e.g. a
<term>
language model score
</term>
) as well as
<term>
binary features
</term>
based on the
<term>
block identities
</term>
themselves , e.g.
<term>
block bigram features
</term>
.
#9968We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. a language model score) as well as binary features based on the block identities themselves, e.g. block bigram features.
measure(ment),20-3-P05-1069,ak
We use a
<term>
maximum likelihood criterion
</term>
to train a
<term>
log-linear block bigram model
</term>
which uses
<term>
real-valued features
</term>
( e.g. a
<term>
language model score
</term>
) as well as
<term>
binary features
</term>
based on the
<term>
block identities
</term>
themselves , e.g.
<term>
block bigram features
</term>
.
#9985We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. a language model score ) as well as binary features based on the block identities themselves, e.g. block bigram features.