measure(ment),21-2-H01-1058,bq |
We find that simple
<term>
interpolation methods
</term>
, like
<term>
log-linear and linear interpolation
</term>
, improve the
<term>
performance
</term>
but fall short of the
<term>
performance
</term>
of an
<term>
oracle
</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. |
measure(ment),19-3-H01-1058,bq |
The
<term>
oracle
</term>
knows the
<term>
reference word string
</term>
and selects the
<term>
word string
</term>
with the best
<term>
performance
</term>
( typically ,
<term>
word or semantic error rate
</term>
) from a list of
<term>
word strings
</term>
, where each
<term>
word string
</term>
has been obtained by using a different
<term>
LM
</term>
.
|
#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 |
The
<term>
oracle
</term>
knows the
<term>
reference word string
</term>
and selects the
<term>
word string
</term>
with the best
<term>
performance
</term>
( typically ,
<term>
word or semantic error rate
</term>
) from a list of
<term>
word strings
</term>
, where each
<term>
word string
</term>
has been obtained by using a different
<term>
LM
</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. |
tech,7-4-H01-1058,bq |
Actually , the
<term>
oracle
</term>
acts like a
<term>
dynamic combiner
</term>
with
<term>
hard decisions
</term>
using the
<term>
reference
</term>
.
|
#1122
Actually, the oracle acts like adynamic combiner with hard decisions using the reference. |
measure(ment),7-7-H01-1058,bq |
The method amounts to tagging
<term>
LMs
</term>
with
<term>
confidence measures
</term>
and picking the best
<term>
hypothesis
</term>
corresponding to the
<term>
LM
</term>
with the best
<term>
confidence
</term>
.
|
#1179
The method amounts to tagging LMs withconfidence measures and picking the best hypothesis corresponding to the LM with the best confidence. |
other,10-4-H01-1058,bq |
Actually , the
<term>
oracle
</term>
acts like 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 |
Actually , the
<term>
oracle
</term>
acts like a
<term>
dynamic combiner
</term>
with
<term>
hard decisions
</term>
using the
<term>
reference
</term>
.
|
#1129
Actually, the oracle acts like a dynamic combiner with hard decisions using thereference. |
tech,4-2-H01-1058,bq |
We find that simple
<term>
interpolation methods
</term>
, like
<term>
log-linear and linear interpolation
</term>
, improve the
<term>
performance
</term>
but fall short of the
<term>
performance
</term>
of an
<term>
oracle
</term>
.
|
#1048
We find that simpleinterpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle. |
model,17-7-H01-1058,bq |
The method amounts to tagging
<term>
LMs
</term>
with
<term>
confidence measures
</term>
and picking the best
<term>
hypothesis
</term>
corresponding to the
<term>
LM
</term>
with the best
<term>
confidence
</term>
.
|
#1189
The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to theLM with the best confidence. |
measure(ment),15-3-H01-1058,bq |
The
<term>
oracle
</term>
knows the
<term>
reference word string
</term>
and selects the
<term>
word string
</term>
with the best
<term>
performance
</term>
( typically ,
<term>
word or semantic error rate
</term>
) from a list of
<term>
word strings
</term>
, where each
<term>
word string
</term>
has been obtained by using a different
<term>
LM
</term>
.
|
#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),15-2-H01-1058,bq |
We find that simple
<term>
interpolation methods
</term>
, like
<term>
log-linear and linear interpolation
</term>
, improve the
<term>
performance
</term>
but fall short of the
<term>
performance
</term>
of an
<term>
oracle
</term>
.
|
#1059
We find that simple interpolation methods, like log-linear and linear interpolation, improve theperformance but fall short of the performance of an oracle. |
tech,17-6-H01-1058,bq |
We suggest a method that mimics the behavior of the
<term>
oracle
</term>
using a
<term>
neural network
</term>
or a
<term>
decision tree
</term>
.
|
#1169
We suggest a method that mimics the behavior of the oracle using a neural network or adecision tree. |
measure(ment),18-5-H01-1058,bq |
We provide experimental results that clearly show the need for a
<term>
dynamic language model combination
</term>
to improve the
<term>
performance
</term>
further .
|
#1149
We provide experimental results that clearly show the need for a dynamic language model combination to improve theperformance further. |
tech,8-2-H01-1058,bq |
We find that simple
<term>
interpolation methods
</term>
, like
<term>
log-linear and linear interpolation
</term>
, improve the
<term>
performance
</term>
but fall short of the
<term>
performance
</term>
of an
<term>
oracle
</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. |
model,11-1-H01-1058,bq |
In this paper , we address the problem of combining several
<term>
language models ( LMs )
</term>
.
|
#1038
In this paper, we address the problem of combining severallanguage models ( LMs ). |
model,5-7-H01-1058,bq |
The method amounts to tagging
<term>
LMs
</term>
with
<term>
confidence measures
</term>
and picking the best
<term>
hypothesis
</term>
corresponding to the
<term>
LM
</term>
with the best
<term>
confidence
</term>
.
|
#1177
The method amounts to taggingLMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence. |
other,13-7-H01-1058,bq |
The method amounts to tagging
<term>
LMs
</term>
with
<term>
confidence measures
</term>
and picking the best
<term>
hypothesis
</term>
corresponding to the
<term>
LM
</term>
with the best
<term>
confidence
</term>
.
|
#1185
The method amounts to tagging LMs with confidence measures and picking the besthypothesis corresponding to the LM with the best confidence. |
tech,11-5-H01-1058,bq |
We provide experimental results that clearly show the need for a
<term>
dynamic language model combination
</term>
to improve the
<term>
performance
</term>
further .
|
#1142
We provide experimental results that clearly show the need for adynamic language model combination to improve the performance further. |
model,43-3-H01-1058,bq |
The
<term>
oracle
</term>
knows the
<term>
reference word string
</term>
and selects the
<term>
word string
</term>
with the best
<term>
performance
</term>
( typically ,
<term>
word or semantic error rate
</term>
) from a list of
<term>
word strings
</term>
, where each
<term>
word string
</term>
has been obtained by using a different
<term>
LM
</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,10-3-H01-1058,bq |
The
<term>
oracle
</term>
knows the
<term>
reference word string
</term>
and selects the
<term>
word string
</term>
with the best
<term>
performance
</term>
( typically ,
<term>
word or semantic error rate
</term>
) from a list of
<term>
word strings
</term>
, where each
<term>
word string
</term>
has been obtained by using a different
<term>
LM
</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. |