The
<term>
paraphrase sets
</term>
produced by this method thus seem adequate as
<term>
reference sets
</term>
to be used for
<term>
MT evaluation
</term>
.
#7682The paraphrase sets produced by this method thus seem adequate as reference sets to be used forMT evaluation.
measure(ment),19-1-I05-5008,ak
We propose a method that automatically generates
<term>
paraphrase sets
</term>
from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in
<term>
objective machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
#7580We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets inobjective machine translation evaluation measures like BLEU and NIST.
measure(ment),25-1-I05-5008,ak
We propose a method that automatically generates
<term>
paraphrase sets
</term>
from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in
<term>
objective machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
#7586We 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.
measure(ment),27-1-I05-5008,ak
We propose a method that automatically generates
<term>
paraphrase sets
</term>
from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in
<term>
objective machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
#7588We 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,1-3-I05-5008,ak
The
<term>
paraphrase sets
</term>
produced by this method thus seem adequate as
<term>
reference sets
</term>
to be used for
<term>
MT evaluation
</term>
.
#7666Theparaphrase sets produced by this method thus seem adequate as reference sets to be used for MT evaluation.
other,10-1-I05-5008,ak
We propose a method that automatically generates
<term>
paraphrase sets
</term>
from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in
<term>
objective machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
#7571We 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.
other,11-3-I05-5008,ak
The
<term>
paraphrase sets
</term>
produced by this method thus seem adequate as
<term>
reference sets
</term>
to be used for
<term>
MT evaluation
</term>
.
#7676The paraphrase sets produced by this method thus seem adequate asreference sets to be used for MT evaluation.
other,16-1-I05-5008,ak
We propose a method that automatically generates
<term>
paraphrase sets
</term>
from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in
<term>
objective machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
#7577We 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.
other,18-2-I05-5008,ak
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct sentences ; ( ii ) their
<term>
equivalence
</term>
in
<term>
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 hand-produced sets .
#7608We 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.
other,31-2-I05-5008,ak
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct sentences ; ( ii ) their
<term>
equivalence
</term>
in
<term>
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 hand-produced sets .
#7621We 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.
other,33-2-I05-5008,ak
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct sentences ; ( ii ) their
<term>
equivalence
</term>
in
<term>
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 hand-produced sets .
#7623We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correct sentences; (ii) their equivalence inmeaning: 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,ak
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct sentences ; ( ii ) their
<term>
equivalence
</term>
in
<term>
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 hand-produced sets .
#7630We 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,43-2-I05-5008,ak
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct sentences ; ( ii ) their
<term>
equivalence
</term>
in
<term>
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 hand-produced sets .
#7633We 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.
other,46-2-I05-5008,ak
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct sentences ; ( ii ) their
<term>
equivalence
</term>
in
<term>
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 hand-produced sets .
#7636We 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.
other,57-2-I05-5008,ak
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct sentences ; ( ii ) their
<term>
equivalence
</term>
in
<term>
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 hand-produced sets .
#7647We 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.
other,6-2-I05-5008,ak
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct sentences ; ( ii ) their
<term>
equivalence
</term>
in
<term>
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 hand-produced sets .
#7596We 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,65-2-I05-5008,ak
We measured the quality of the
<term>
paraphrases
</term>
produced in an experiment , i.e. , ( i ) their
<term>
grammaticality
</term>
: at least 99 % correct sentences ; ( ii ) their
<term>
equivalence
</term>
in
<term>
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 hand-produced sets .
#7655We 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.
other,7-1-I05-5008,ak
We propose a method that automatically generates
<term>
paraphrase sets
</term>
from
<term>
seed sentences
</term>
to be used as
<term>
reference sets
</term>
in
<term>
objective machine translation evaluation measures
</term>
like
<term>
BLEU
</term>
and
<term>
NIST
</term>
.
#7568We 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.