tech,6-4-N01-1003,bq |
First , a very simple ,
<term>
randomized sentence-plan-generator ( SPG )
</term>
generates a potentially large list of possible
<term>
sentence plans
</term>
for a given
<term>
text-plan input
</term>
.
|
#1380
First, a very simple,randomized sentence-plan-generator ( SPG ) generates a potentially large list of possible sentence plans for a given text-plan input. |
tech,0-1-N01-1003,bq |
Our
<term>
algorithm
</term>
reported more than 99 %
<term>
accuracy
</term>
in both
<term>
language identification
</term>
and
<term>
key prediction
</term>
.
<term>
Sentence planning
</term>
is a set of inter-related but distinct tasks , one of which is
<term>
sentence scoping
</term>
, i.e. the choice of
<term>
syntactic structure
</term>
for elementary
<term>
speech acts
</term>
and the decision of how to combine them into one or more
<term>
sentences
</term>
.
|
#1293
Our algorithm reported more than 99% accuracy in both language identification and key prediction.Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. |
tech,9-2-N01-1003,bq |
In this paper , we present
<term>
SPoT
</term>
, a
<term>
sentence planner
</term>
, and a new methodology for automatically training
<term>
SPoT
</term>
on the basis of
<term>
feedback
</term>
provided by
<term>
human judges
</term>
.
|
#1344
In this paper, we present SPoT, asentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. |
other,10-7-N01-1003,bq |
We show that the trained
<term>
SPR
</term>
learns to select a
<term>
sentence plan
</term>
whose
<term>
rating
</term>
on average is only 5 % worse than the
<term>
top human-ranked sentence plan
</term>
.
|
#1443
We show that the trained SPR learns to select asentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan. |
other,27-2-N01-1003,bq |
In this paper , we present
<term>
SPoT
</term>
, a
<term>
sentence planner
</term>
, and a new methodology for automatically training
<term>
SPoT
</term>
on the basis of
<term>
feedback
</term>
provided by
<term>
human judges
</term>
.
|
#1362
In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided byhuman judges. |
other,26-1-N01-1003,bq |
<term>
Sentence planning
</term>
is a set of inter-related but distinct tasks , one of which is
<term>
sentence scoping
</term>
, i.e. the choice of
<term>
syntactic structure
</term>
for elementary
<term>
speech acts
</term>
and the decision of how to combine them into one or more
<term>
sentences
</term>
.
|
#1319
Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementaryspeech acts and the decision of how to combine them into one or more sentences. |
lr,8-6-N01-1003,bq |
The
<term>
SPR
</term>
uses
<term>
ranking rules
</term>
automatically learned from
<term>
training data
</term>
.
|
#1430
The SPR uses ranking rules automatically learned fromtraining data. |
other,23-4-N01-1003,bq |
First , a very simple ,
<term>
randomized sentence-plan-generator ( SPG )
</term>
generates a potentially large list of possible
<term>
sentence plans
</term>
for a given
<term>
text-plan input
</term>
.
|
#1397
First, a very simple, randomized sentence-plan-generator (SPG) generates a potentially large list of possible sentence plans for a giventext-plan input. |
tech,15-1-N01-1003,bq |
<term>
Sentence planning
</term>
is a set of inter-related but distinct tasks , one of which is
<term>
sentence scoping
</term>
, i.e. the choice of
<term>
syntactic structure
</term>
for elementary
<term>
speech acts
</term>
and the decision of how to combine them into one or more
<term>
sentences
</term>
.
|
#1308
Sentence planning is a set of inter-related but distinct tasks, one of which issentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. |
other,40-1-N01-1003,bq |
<term>
Sentence planning
</term>
is a set of inter-related but distinct tasks , one of which is
<term>
sentence scoping
</term>
, i.e. the choice of
<term>
syntactic structure
</term>
for elementary
<term>
speech acts
</term>
and the decision of how to combine them into one or more
<term>
sentences
</term>
.
|
#1333
Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or moresentences. |
other,24-2-N01-1003,bq |
In this paper , we present
<term>
SPoT
</term>
, a
<term>
sentence planner
</term>
, and a new methodology for automatically training
<term>
SPoT
</term>
on the basis of
<term>
feedback
</term>
provided by
<term>
human judges
</term>
.
|
#1359
In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis offeedback provided by human judges. |
other,22-1-N01-1003,bq |
<term>
Sentence planning
</term>
is a set of inter-related but distinct tasks , one of which is
<term>
sentence scoping
</term>
, i.e. the choice of
<term>
syntactic structure
</term>
for elementary
<term>
speech acts
</term>
and the decision of how to combine them into one or more
<term>
sentences
</term>
.
|
#1315
Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice ofsyntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. |
other,12-5-N01-1003,bq |
Second , the
<term>
sentence-plan-ranker ( SPR )
</term>
ranks the list of output
<term>
sentence plans
</term>
, and then selects the top-ranked
<term>
plan
</term>
.
|
#1412
Second, the sentence-plan-ranker (SPR) ranks the list of outputsentence plans, and then selects the top-ranked plan. |
other,18-4-N01-1003,bq |
First , a very simple ,
<term>
randomized sentence-plan-generator ( SPG )
</term>
generates a potentially large list of possible
<term>
sentence plans
</term>
for a given
<term>
text-plan input
</term>
.
|
#1392
First, a very simple, randomized sentence-plan-generator (SPG) generates a potentially large list of possiblesentence plans for a given text-plan input. |
tool,6-2-N01-1003,bq |
In this paper , we present
<term>
SPoT
</term>
, a
<term>
sentence planner
</term>
, and a new methodology for automatically training
<term>
SPoT
</term>
on the basis of
<term>
feedback
</term>
provided by
<term>
human judges
</term>
.
|
#1341
In this paper, we presentSPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. |
tech,3-5-N01-1003,bq |
Second , the
<term>
sentence-plan-ranker ( SPR )
</term>
ranks the list of output
<term>
sentence plans
</term>
, and then selects the top-ranked
<term>
plan
</term>
.
|
#1403
Second, thesentence-plan-ranker ( SPR ) ranks the list of output sentence plans, and then selects the top-ranked plan. |
tech,1-6-N01-1003,bq |
The
<term>
SPR
</term>
uses
<term>
ranking rules
</term>
automatically learned from
<term>
training data
</term>
.
|
#1423
TheSPR uses ranking rules automatically learned from training data. |
other,23-7-N01-1003,bq |
We show that the trained
<term>
SPR
</term>
learns to select a
<term>
sentence plan
</term>
whose
<term>
rating
</term>
on average is only 5 % worse than the
<term>
top human-ranked sentence plan
</term>
.
|
#1456
We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than thetop human-ranked sentence plan. |
other,20-5-N01-1003,bq |
Second , the
<term>
sentence-plan-ranker ( SPR )
</term>
ranks the list of output
<term>
sentence plans
</term>
, and then selects the top-ranked
<term>
plan
</term>
.
|
#1420
Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans, and then selects the top-rankedplan. |
tech,5-7-N01-1003,bq |
We show that the trained
<term>
SPR
</term>
learns to select a
<term>
sentence plan
</term>
whose
<term>
rating
</term>
on average is only 5 % worse than the
<term>
top human-ranked sentence plan
</term>
.
|
#1438
We show that the trainedSPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan. |