model,11-1-H01-1058,bq |
address the problem of combining several
<term>
|
language models ( LMs )
|
</term>
. We find that simple
<term>
interpolation
|
#1038
In this paper, we address the problem of combining severallanguage models ( LMs ). |
tech,4-2-H01-1058,bq |
LMs )
</term>
. We find that simple
<term>
|
interpolation methods
|
</term>
, like
<term>
log-linear and linear
|
#1048
We find that simpleinterpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle. |
tech,8-2-H01-1058,bq |
interpolation methods
</term>
, like
<term>
|
log-linear and linear interpolation
|
</term>
, improve the
<term>
performance
</term>
|
#1052
We find that simple interpolation methods, likelog-linear and linear interpolation, improve the performance but fall short of the performance of an oracle. |
measure(ment),15-2-H01-1058,bq |
interpolation
</term>
, improve the
<term>
|
performance
|
</term>
but fall short of the
<term>
performance
|
#1059
We find that simple interpolation methods, like log-linear and linear interpolation, improve theperformance but fall short of the performance of an oracle. |
measure(ment),21-2-H01-1058,bq |
performance
</term>
but fall short of the
<term>
|
performance
|
</term>
of an
<term>
oracle
</term>
. The
<term>
|
#1065
We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of theperformance of an oracle. |
other,24-2-H01-1058,bq |
of the
<term>
performance
</term>
of an
<term>
|
oracle
|
</term>
. The
<term>
oracle
</term>
knows the
|
#1068
We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of anoracle. |
other,1-3-H01-1058,bq |
</term>
of an
<term>
oracle
</term>
. The
<term>
|
oracle
|
</term>
knows the
<term>
reference word string
|
#1071
Theoracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. |
other,4-3-H01-1058,bq |
</term>
. The
<term>
oracle
</term>
knows the
<term>
|
reference word string
|
</term>
and selects the
<term>
word string
</term>
|
#1074
The oracle knows thereference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. |
other,10-3-H01-1058,bq |
word string
</term>
and selects the
<term>
|
word string
|
</term>
with the best
<term>
performance
</term>
|
#1080
The oracle knows the reference word string and selects theword string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. |
measure(ment),15-3-H01-1058,bq |
<term>
word string
</term>
with the best
<term>
|
performance
|
</term>
( typically ,
<term>
word or semantic
|
#1085
The oracle knows the reference word string and selects the word string with the bestperformance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. |
measure(ment),19-3-H01-1058,bq |
<term>
performance
</term>
( typically ,
<term>
|
word or semantic error rate
|
</term>
) from a list of
<term>
word strings
|
#1089
The oracle knows the reference word string and selects the word string with the best performance (typically,word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. |
other,29-3-H01-1058,bq |
error rate
</term>
) from a list of
<term>
|
word strings
|
</term>
, where each
<term>
word string
</term>
|
#1099
The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list ofword strings, where each word string has been obtained by using a different LM. |
other,34-3-H01-1058,bq |
<term>
word strings
</term>
, where each
<term>
|
word string
|
</term>
has been obtained by using a different
|
#1104
The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where eachword string has been obtained by using a different LM. |
model,43-3-H01-1058,bq |
been obtained by using a different
<term>
|
LM
|
</term>
. Actually , the
<term>
oracle
</term>
|
#1113
The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a differentLM. |
other,3-4-H01-1058,bq |
different
<term>
LM
</term>
. Actually , the
<term>
|
oracle
|
</term>
acts like a
<term>
dynamic combiner
|
#1118
Actually, theoracle acts like a dynamic combiner with hard decisions using the reference. |
tech,7-4-H01-1058,bq |
the
<term>
oracle
</term>
acts like a
<term>
|
dynamic combiner
|
</term>
with
<term>
hard decisions
</term>
using
|
#1122
Actually, the oracle acts like adynamic combiner with hard decisions using the reference. |
other,10-4-H01-1058,bq |
a
<term>
dynamic combiner
</term>
with
<term>
|
hard decisions
|
</term>
using the
<term>
reference
</term>
.
|
#1125
Actually, the oracle acts like a dynamic combiner withhard decisions using the reference. |
other,14-4-H01-1058,bq |
<term>
hard decisions
</term>
using the
<term>
|
reference
|
</term>
. We provide experimental results
|
#1129
Actually, the oracle acts like a dynamic combiner with hard decisions using thereference. |
tech,11-5-H01-1058,bq |
results that clearly show the need for a
<term>
|
dynamic language model combination
|
</term>
to improve the
<term>
performance
</term>
|
#1142
We provide experimental results that clearly show the need for adynamic language model combination to improve the performance further. |
measure(ment),18-5-H01-1058,bq |
model combination
</term>
to improve the
<term>
|
performance
|
</term>
further . We suggest a method that
|
#1149
We provide experimental results that clearly show the need for a dynamic language model combination to improve theperformance further. |