measure(ment),25-1-I05-5008,bq |
We propose a
<term>
method
</term>
that automatically generates
<term>
paraphrase
</term>
sets from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in objective
<term>
machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
|
#8466
We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures likeBLEU and NIST. |
other,46-2-I05-5008,bq |
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct
<term>
sentences
</term>
; ( ii ) their
<term>
equivalence in meaning
</term>
: at least 96 % correct
<term>
paraphrases
</term>
either by
<term>
meaning equivalence
</term>
or
<term>
entailment
</term>
; and , ( iii ) the amount of internal
<term>
lexical and syntactical variation
</term>
in a set of
<term>
paraphrases
</term>
: slightly superior to that of
<term>
hand-produced sets
</term>
.
|
#8516
We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correct sentences; (ii) their equivalence in meaning: at least 96% correct paraphrases either by meaning equivalence orentailment; and, (iii) the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. |
measure(ment),31-2-I05-5008,bq |
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct
<term>
sentences
</term>
; ( ii ) their
<term>
equivalence in meaning
</term>
: at least 96 % correct
<term>
paraphrases
</term>
either by
<term>
meaning equivalence
</term>
or
<term>
entailment
</term>
; and , ( iii ) the amount of internal
<term>
lexical and syntactical variation
</term>
in a set of
<term>
paraphrases
</term>
: slightly superior to that of
<term>
hand-produced sets
</term>
.
|
#8501
We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correct sentences; (ii) theirequivalence in meaning: at least 96% correct paraphrases either by meaning equivalence or entailment; and, (iii) the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. |
measure(ment),18-2-I05-5008,bq |
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct
<term>
sentences
</term>
; ( ii ) their
<term>
equivalence in meaning
</term>
: at least 96 % correct
<term>
paraphrases
</term>
either by
<term>
meaning equivalence
</term>
or
<term>
entailment
</term>
; and , ( iii ) the amount of internal
<term>
lexical and syntactical variation
</term>
in a set of
<term>
paraphrases
</term>
: slightly superior to that of
<term>
hand-produced sets
</term>
.
|
#8488
We measured the quality of the paraphrases produced in an experiment, i.e., (i) theirgrammaticality: at least 99% correct sentences; (ii) their equivalence in meaning: at least 96% correct paraphrases either by meaning equivalence or entailment; and, (iii) the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. |
lr,72-2-I05-5008,bq |
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct
<term>
sentences
</term>
; ( ii ) their
<term>
equivalence in meaning
</term>
: at least 96 % correct
<term>
paraphrases
</term>
either by
<term>
meaning equivalence
</term>
or
<term>
entailment
</term>
; and , ( iii ) the amount of internal
<term>
lexical and syntactical variation
</term>
in a set of
<term>
paraphrases
</term>
: slightly superior to that of
<term>
hand-produced sets
</term>
.
|
#8542
We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correct sentences; (ii) their equivalence in meaning: at least 96% correct paraphrases either by meaning equivalence or entailment; and, (iii) the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that ofhand-produced sets. |
other,57-2-I05-5008,bq |
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct
<term>
sentences
</term>
; ( ii ) their
<term>
equivalence in meaning
</term>
: at least 96 % correct
<term>
paraphrases
</term>
either by
<term>
meaning equivalence
</term>
or
<term>
entailment
</term>
; and , ( iii ) the amount of internal
<term>
lexical and syntactical variation
</term>
in a set of
<term>
paraphrases
</term>
: slightly superior to that of
<term>
hand-produced sets
</term>
.
|
#8527
We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correct sentences; (ii) their equivalence in meaning: at least 96% correct paraphrases either by meaning equivalence or entailment; and, (iii) the amount of internallexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. |
measure(ment),20-1-I05-5008,bq |
We propose a
<term>
method
</term>
that automatically generates
<term>
paraphrase
</term>
sets from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in objective
<term>
machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
|
#8461
We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objectivemachine translation evaluation measures like BLEU and NIST. |
other,43-2-I05-5008,bq |
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct
<term>
sentences
</term>
; ( ii ) their
<term>
equivalence in meaning
</term>
: at least 96 % correct
<term>
paraphrases
</term>
either by
<term>
meaning equivalence
</term>
or
<term>
entailment
</term>
; and , ( iii ) the amount of internal
<term>
lexical and syntactical variation
</term>
in a set of
<term>
paraphrases
</term>
: slightly superior to that of
<term>
hand-produced sets
</term>
.
|
#8513
We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correct sentences; (ii) their equivalence in meaning: at least 96% correct paraphrases either bymeaning equivalence or entailment; and, (iii) the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. |
tech,3-1-I05-5008,bq |
We propose a
<term>
method
</term>
that automatically generates
<term>
paraphrase
</term>
sets from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in objective
<term>
machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
|
#8444
We propose amethod that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST. |
tech,6-3-I05-5008,bq |
The
<term>
paraphrase
</term>
sets produced by this
<term>
method
</term>
thus seem adequate as
<term>
reference sets
</term>
to be used for
<term>
MT evaluation
</term>
.
|
#8551
The paraphrase sets produced by thismethod thus seem adequate as reference sets to be used for MT evaluation. |
measure(ment),17-3-I05-5008,bq |
The
<term>
paraphrase
</term>
sets produced by this
<term>
method
</term>
thus seem adequate as
<term>
reference sets
</term>
to be used for
<term>
MT evaluation
</term>
.
|
#8562
The paraphrase sets produced by this method thus seem adequate as reference sets to be used forMT evaluation. |
measure(ment),27-1-I05-5008,bq |
We propose a
<term>
method
</term>
that automatically generates
<term>
paraphrase
</term>
sets from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in objective
<term>
machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
|
#8468
We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU andNIST. |
other,7-1-I05-5008,bq |
We propose a
<term>
method
</term>
that automatically generates
<term>
paraphrase
</term>
sets from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in objective
<term>
machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
|
#8448
We propose a method that automatically generatesparaphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST. |
other,1-3-I05-5008,bq |
The
<term>
paraphrase
</term>
sets produced by this
<term>
method
</term>
thus seem adequate as
<term>
reference sets
</term>
to be used for
<term>
MT evaluation
</term>
.
|
#8546
Theparaphrase sets produced by this method thus seem adequate as reference sets to be used for MT evaluation. |
other,6-2-I05-5008,bq |
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct
<term>
sentences
</term>
; ( ii ) their
<term>
equivalence in meaning
</term>
: at least 96 % correct
<term>
paraphrases
</term>
either by
<term>
meaning equivalence
</term>
or
<term>
entailment
</term>
; and , ( iii ) the amount of internal
<term>
lexical and syntactical variation
</term>
in a set of
<term>
paraphrases
</term>
: slightly superior to that of
<term>
hand-produced sets
</term>
.
|
#8476
We measured the quality of theparaphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correct sentences; (ii) their equivalence in meaning: at least 96% correct paraphrases either by meaning equivalence or entailment; and, (iii) the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. |
other,40-2-I05-5008,bq |
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct
<term>
sentences
</term>
; ( ii ) their
<term>
equivalence in meaning
</term>
: at least 96 % correct
<term>
paraphrases
</term>
either by
<term>
meaning equivalence
</term>
or
<term>
entailment
</term>
; and , ( iii ) the amount of internal
<term>
lexical and syntactical variation
</term>
in a set of
<term>
paraphrases
</term>
: slightly superior to that of
<term>
hand-produced sets
</term>
.
|
#8510
We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correct sentences; (ii) their equivalence in meaning: at least 96% correctparaphrases either by meaning equivalence or entailment; and, (iii) the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. |
other,65-2-I05-5008,bq |
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct
<term>
sentences
</term>
; ( ii ) their
<term>
equivalence in meaning
</term>
: at least 96 % correct
<term>
paraphrases
</term>
either by
<term>
meaning equivalence
</term>
or
<term>
entailment
</term>
; and , ( iii ) the amount of internal
<term>
lexical and syntactical variation
</term>
in a set of
<term>
paraphrases
</term>
: slightly superior to that of
<term>
hand-produced sets
</term>
.
|
#8535
We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correct sentences; (ii) their equivalence in meaning: at least 96% correct paraphrases either by meaning equivalence or entailment; and, (iii) the amount of internal lexical and syntactical variation in a set ofparaphrases: slightly superior to that of hand-produced sets. |
lr,16-1-I05-5008,bq |
We propose a
<term>
method
</term>
that automatically generates
<term>
paraphrase
</term>
sets from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in objective
<term>
machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
|
#8457
We propose a method that automatically generates paraphrase sets from seed sentences to be used asreference sets in objective machine translation evaluation measures like BLEU and NIST. |
lr,11-3-I05-5008,bq |
The
<term>
paraphrase
</term>
sets produced by this
<term>
method
</term>
thus seem adequate as
<term>
reference sets
</term>
to be used for
<term>
MT evaluation
</term>
.
|
#8556
The paraphrase sets produced by this method thus seem adequate asreference sets to be used for MT evaluation. |
other,10-1-I05-5008,bq |
We propose a
<term>
method
</term>
that automatically generates
<term>
paraphrase
</term>
sets from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in objective
<term>
machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
|
#8451
We propose a method that automatically generates paraphrase sets fromseed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST. |