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Decem
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20
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
5
4
3
-
5
5
5
4
5544
C
o
n
s
eq
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tly
,
t
h
e
d
ev
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m
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W
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m
p
ar
ed
to
tr
ad
itio
n
al
ap
p
r
o
ac
h
e
s
lik
e
n
aïv
e
B
ay
es,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SV
M
)
,
an
d
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
n
etwo
r
k
(
L
STM
)
.
I
ts
p
r
e
-
tr
ain
ed
ar
ch
itectu
r
e
an
d
ab
ilit
y
t
o
f
in
e
-
tu
n
e
g
iv
e
B
E
R
T
an
ad
v
an
tag
e
o
v
e
r
o
th
er
tech
n
iq
u
es,
ac
h
iev
in
g
h
ig
h
er
p
er
f
o
r
m
a
n
ce
i
n
m
etr
ics
s
u
ch
as
F1
s
co
r
e,
p
r
ec
is
io
n
,
an
d
r
ec
all
.
T
h
e
r
esear
ch
[
1
0
]
p
r
esen
ts
a
f
r
am
ewo
r
k
th
at
lev
er
ag
es
b
o
t
h
g
en
er
ativ
e
p
r
e
-
tr
ain
ed
tr
an
s
f
o
r
m
er
s
(
GPT
)
an
d
B
E
R
T
f
o
r
ef
f
ec
tiv
e
f
ak
e
n
ews
d
etec
tio
n
,
ac
h
iev
in
g
9
5
.
3
0
%
ac
cu
r
ac
y
in
test
s
co
n
d
u
cted
o
n
two
r
ea
l
-
wo
r
l
d
d
atasets
,
wh
ich
em
p
h
asizes
its
s
tr
o
n
g
p
o
te
n
tial
to
ad
d
r
ess
th
e
is
s
u
e
o
f
f
ak
e
n
ews in
th
e
d
ig
it
al
er
a.
Desp
ite
it
s
p
o
ten
tial,
ap
p
ly
in
g
B
E
R
T
f
o
r
d
etec
tin
g
AI
-
g
en
er
ated
ess
ay
s
r
em
ain
s
c
h
allen
g
in
g
,
p
r
im
ar
ily
d
u
e
to
th
e
co
m
p
le
x
ity
in
v
o
lv
e
d
in
f
in
e
-
tu
n
in
g
th
e
m
o
d
el'
s
p
ar
am
eter
s
.
T
h
is
is
ev
id
en
ce
d
b
y
t
h
e
r
esear
ch
,
wh
ich
d
em
o
n
s
tr
ate
s
th
at
B
E
R
T
p
ar
am
eter
s
ca
n
b
e
o
p
tim
ized
u
s
in
g
t
h
e
wh
ale
o
p
tim
izatio
n
alg
o
r
ith
m
f
o
r
s
em
an
tic
tex
t
g
e
n
er
atio
n
;
h
o
wev
er
,
th
e
r
esu
lts
s
till
h
o
v
er
ar
o
u
n
d
0
.
7
6
o
n
m
et
r
ics
f
o
r
ev
alu
atio
n
o
f
tr
an
s
latio
n
,
lik
e
GPT
-
2
[
1
1
]
.
T
h
e
s
elec
tio
n
o
f
o
p
tim
al
p
ar
am
eter
s
in
B
E
R
T
is
cr
itical
to
m
ax
im
izin
g
m
o
d
e
l
p
er
f
o
r
m
an
ce
[
8
]
.
Ho
we
v
er
,
th
is
p
r
o
ce
s
s
is
o
f
te
n
p
er
f
o
r
m
ed
m
an
u
all
y
o
r
t
h
r
o
u
g
h
tr
ial
-
an
d
-
er
r
o
r
tech
n
iq
u
es,
wh
ich
ar
e
n
o
t
o
n
ly
in
ef
f
ic
ien
t
b
u
t
also
h
ig
h
ly
tim
e
-
c
o
n
s
u
m
in
g
.
B
E
R
T
r
elies
o
n
a
v
ast
ar
r
ay
o
f
h
y
p
er
p
ar
am
eter
s
th
at
c
r
itically
in
f
lu
e
n
ce
its
p
er
f
o
r
m
an
ce
,
s
u
ch
as
th
e
lear
n
in
g
r
ate,
n
u
m
b
er
o
f
lay
er
s
,
em
b
ed
d
in
g
s
ize,
an
d
b
atc
h
s
ize
[
1
2
]
,
[
1
3
]
.
Attain
in
g
o
p
ti
m
al
p
er
f
o
r
m
an
ce
d
em
a
n
d
s
a
m
o
r
e
s
tr
u
ctu
r
ed
a
n
d
m
eth
o
d
ical
ap
p
r
o
ac
h
to
s
elec
tin
g
th
e
m
o
s
t su
itab
le
p
ar
am
eter
s
.
T
h
is
r
esear
ch
u
tili
ze
s
a
m
etah
eu
r
is
tic
o
p
tim
izatio
n
m
eth
o
d
,
n
am
ely
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
,
to
tack
le
th
e
d
if
f
icu
lties
ass
o
ciate
d
wi
th
o
p
tim
izin
g
B
E
R
T
'
s
p
ar
am
eter
s
elec
tio
n
.
PS
O
in
s
p
ir
ed
b
y
th
e
b
eh
av
io
r
o
f
b
ir
d
f
lo
ck
s
an
d
f
is
h
s
ch
o
o
ls
,
ef
f
ec
tiv
ely
n
a
v
ig
ate
s
co
m
p
lex
s
ea
r
ch
s
p
ac
es
an
d
i
s
u
s
ef
u
l
f
o
r
tu
n
in
g
h
y
p
er
p
ar
am
eter
s
in
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
lear
n
in
g
m
o
d
els
[
1
4
]
.
T
h
e
s
tu
d
y
b
y
[
1
5
]
s
u
g
g
ests
th
at
in
teg
r
atin
g
PS
O
with
r
ec
u
r
s
i
v
e
f
ea
tu
r
e
elim
in
atio
n
(
R
FE
)
f
o
r
o
p
tim
izin
g
SVM
p
ar
am
e
ter
s
en
h
an
ce
s
h
ea
r
t
d
is
ea
s
e
d
etec
tio
n
ac
cu
r
ac
y
f
r
o
m
8
6
.
4
1
%
to
8
9
.
1
3
%,
em
p
h
as
izin
g
PS
O
'
s
g
r
ea
ter
ef
f
ec
tiv
en
ess
o
v
er
tr
ad
itio
n
al
m
eth
o
d
s
.
I
n
ad
d
itio
n
,
th
e
r
es
ea
r
ch
b
y
[
1
6
]
in
tr
o
d
u
ce
s
an
i
n
n
o
v
ativ
e
tier
e
d
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
e
th
at
lev
er
ag
es
PS
O
alo
n
g
s
id
e
g
en
etic
alg
o
r
ith
m
(
GA)
to
o
p
ti
m
ize
th
e
p
er
f
o
r
m
an
ce
o
f
c
o
r
o
n
ar
y
h
ea
r
t
d
is
ea
s
e
d
iag
n
o
s
is
s
y
s
tem
s
.
T
h
e
ap
p
li
ca
tio
n
o
f
PS
O
y
ield
ed
r
em
ar
k
ab
le
o
u
tc
o
m
es,
with
an
ac
c
u
r
ac
y
o
f
9
6
.
0
4
%,
an
AUC
o
f
9
9
.
9
7
%,
a
n
d
a
s
en
s
itiv
ity
o
f
9
8
.
3
6
%,
h
ig
h
lig
h
tin
g
its
co
n
s
id
er
ab
le
p
o
te
n
tial
to
en
h
an
ce
d
iag
n
o
s
tic
ac
cu
r
ac
y
a
n
d
r
eliab
ilit
y
i
n
co
r
o
n
ar
y
h
ea
r
t
d
is
ea
s
e
d
etec
tio
n
.
T
h
e
u
s
e
o
f
PS
O
en
ab
les
a
m
o
r
e
e
f
f
icien
t
an
d
s
y
s
tem
atic
ap
p
r
o
ac
h
to
p
ar
am
eter
o
p
tim
izatio
n
,
s
u
b
s
tan
tially
r
ed
u
cin
g
t
h
e
r
el
ian
ce
o
n
m
an
u
al
ex
p
er
im
en
tatio
n
,
wh
ich
is
o
f
te
n
tim
e
-
co
n
s
u
m
i
n
g
an
d
r
eso
u
r
c
e
in
ten
s
iv
e.
T
h
e
co
m
b
in
atio
n
o
f
B
E
R
T
an
d
PS
O
is
ex
p
ec
ted
to
s
ig
n
if
ican
tly
en
h
a
n
ce
th
e
ef
f
ec
t
iv
en
ess
o
f
AI
-
g
en
er
ated
ess
ay
d
etec
tio
n
s
y
s
tem
s
.
W
h
ile
PS
O
au
to
m
at
es
th
e
o
p
tim
izatio
n
o
f
p
ar
am
eter
co
n
f
ig
u
r
atio
n
s
,
B
E
R
T
p
r
o
v
id
es
a
s
tr
o
n
g
f
o
u
n
d
atio
n
al
m
o
d
el
f
o
r
co
m
p
r
e
h
e
n
d
in
g
th
e
s
tr
u
ctu
r
e
an
d
s
em
a
n
tics
o
f
th
e
te
x
t.
B
y
ap
p
ly
in
g
th
is
m
eth
o
d
,
it
is
ex
p
ec
ted
th
at
th
e
d
etec
tio
n
o
f
AI
-
g
en
er
ated
ess
ay
s
will
b
ec
o
m
e
m
o
r
e
ac
cu
r
ate
an
d
r
eliab
le.
Mo
r
eo
v
er
,
th
e
f
i
n
d
in
g
s
f
r
o
m
th
is
s
tu
d
y
a
r
e
ex
p
ec
te
d
to
d
r
iv
e
t
h
e
d
e
v
elo
p
m
e
n
t
o
f
m
o
r
e
ad
v
an
ce
d
an
d
co
n
tex
t
-
s
p
ec
if
ic
a
u
to
m
ated
d
etec
tio
n
to
o
ls
,
p
ar
ticu
lar
ly
with
in
th
e
e
d
u
ca
tio
n
al
s
ec
to
r
,
wh
er
e
AI
is
in
cr
ea
s
in
g
ly
u
s
ed
to
p
r
o
d
u
ce
ac
ad
em
ic
co
n
ten
t
lik
e
ess
ay
s
.
Fu
r
th
er
m
o
r
e
,
th
e
g
r
o
win
g
ad
o
p
tio
n
o
f
AI
f
o
r
tex
t
g
en
er
atio
n
h
as
r
aised
co
n
ce
r
n
s
ab
o
u
t
ac
ad
em
ic
in
te
g
r
ity
[
1
7
]
.
I
n
e
d
u
ca
tio
n
al
in
s
titu
tio
n
s
,
e
s
s
ay
s
g
en
er
ated
b
y
AI
ca
n
o
b
s
cu
r
e
th
e
d
is
tin
ctio
n
b
etwe
en
au
th
en
tic
s
tu
d
en
t
w
o
r
k
an
d
AI
-
cr
ea
ted
co
n
ten
t
[
1
8
]
–
[
2
0
]
.
T
h
er
ef
o
r
e,
th
e
d
ev
elo
p
m
en
t
o
f
s
y
s
tem
s
ca
p
ab
le
o
f
ac
c
u
r
ately
d
etec
ti
n
g
AI
-
g
e
n
er
ated
ess
ay
s
h
as
b
ec
o
m
e
in
cr
ea
s
in
g
ly
im
p
o
r
tan
t
to
p
r
eser
v
e
t
h
e
cr
e
d
ib
ilit
y
o
f
ac
a
d
em
ic
ev
alu
ati
o
n
s
.
I
n
o
u
r
r
esear
ch
,
we
s
u
g
g
est
th
at
lev
er
ag
in
g
o
p
tim
izatio
n
tech
n
iq
u
es
s
u
ch
as
PS
O
to
f
in
e
-
tu
n
e
B
E
R
T
p
ar
am
eter
s
ca
n
g
r
ea
tly
en
h
a
n
ce
th
e
s
p
ee
d
an
d
ac
cu
r
ac
y
o
f
d
etec
tin
g
AI
-
g
e
n
e
r
ated
ess
ay
s
,
th
er
e
b
y
c
o
n
tr
ib
u
t
in
g
s
ig
n
if
ican
tly
to
th
e
p
r
eser
v
atio
n
o
f
ac
a
d
em
ic
in
teg
r
ity
.
T
h
r
o
u
g
h
th
is
in
teg
r
a
tio
n
,
th
e
r
esear
ch
aim
s
to
p
r
o
v
id
e
a
m
o
r
e
e
f
f
ec
tiv
e
s
o
lu
tio
n
f
o
r
im
p
r
o
v
i
n
g
b
o
th
th
e
ac
cu
r
ac
y
a
n
d
e
f
f
icien
cy
o
f
d
etec
tin
g
AI
-
g
en
er
ate
d
te
x
ts
.
Mo
r
eo
v
er
,
o
u
r
r
esear
ch
h
as
th
e
p
o
ten
tial
to
p
r
o
v
id
e
f
r
esh
in
s
ig
h
ts
in
to
s
y
s
tem
atic
s
tr
ateg
ies
f
o
r
o
p
tim
izin
g
p
ar
am
ete
r
s
in
d
ee
p
lea
r
n
in
g
m
o
d
els,
wh
ich
co
u
ld
b
e
ex
ten
d
e
d
to
a
r
an
g
e
o
f
f
u
t
u
r
e
NL
P
ap
p
licatio
n
s
.
T
h
is
co
n
tr
ib
u
tio
n
will
ex
p
an
d
t
h
e
cu
r
r
e
n
t
liter
atu
r
e
o
n
AI
ad
v
a
n
ce
m
en
ts
in
ed
u
ca
tio
n
,
p
ar
ticu
lar
ly
in
p
r
es
er
v
in
g
th
e
a
u
th
en
ticity
o
f
wr
itten
wo
r
k
in
a
n
in
cr
ea
s
in
g
ly
d
ig
italized
wo
r
ld
.
I
n
s
u
m
m
a
r
y
,
t
h
i
s
r
es
e
a
r
c
h
a
d
d
r
e
s
s
e
s
t
h
e
g
a
p
i
n
e
f
f
e
c
t
i
v
e
,
r
e
a
l
-
t
i
m
e
s
y
s
t
e
m
s
f
o
r
d
e
t
e
ct
i
n
g
AI
-
g
e
n
e
r
a
t
e
d
a
c
a
d
e
m
i
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J E
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&
C
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m
p
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n
g
I
SS
N:
2088
-
8
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2.
M
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H
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t
ad
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ce
m
en
ts
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tr
an
s
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er
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ased
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o
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e
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ig
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ican
t
im
p
r
o
v
e
m
en
ts
in
v
ar
io
u
s
NL
P
task
s
[
2
1
]
.
B
E
R
T
h
as
b
e
co
m
e
a
f
o
u
n
d
atio
n
al
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o
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el
f
o
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an
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u
e
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ee
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ir
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al
en
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r
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alter
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ati
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m
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els
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ch
as
R
o
B
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a,
T
5
,
a
n
d
GPT
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s
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ies
m
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e
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o
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h
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s
B
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R
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y
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e
Nex
t
Sen
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Pre
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ictio
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jectiv
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d
tr
ain
i
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ata
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atch
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ak
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r
e
r
o
b
u
s
t
in
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n
d
er
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d
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g
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n
tex
t
[
2
2
]
.
T
5
,
o
n
th
e
o
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e
r
h
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d
,
r
ef
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r
m
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lates
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s
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t
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at,
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er
f
o
r
m
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ce
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g
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e
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ativ
e
ta
s
k
s
[
2
3
]
.
GPT
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b
ased
m
o
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els,
esp
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ially
th
e
latest
GPT
-
3
an
d
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4
,
o
f
f
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cin
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lik
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es
[
2
4
]
.
Desp
ite
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ativ
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en
g
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s
,
th
eir
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ag
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class
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task
s
r
eq
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ir
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in
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t
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p
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ee
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n
co
m
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ar
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o
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B
E
R
T
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ain
s
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s
tr
o
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g
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ice
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o
r
b
i
n
ar
y
cla
s
s
if
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d
u
e
to
its
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r
etr
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ed
ar
ch
itectu
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d
atten
tio
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to
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o
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tex
tu
al
s
em
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t
ics.
Ho
wev
er
,
f
i
n
e
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tu
n
i
n
g
B
E
R
T
ef
f
ec
tiv
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e
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ain
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a
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u
e
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ast
h
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er
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ar
am
eter
s
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ac
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O,
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o
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ith
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s
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ir
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s
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ee
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eth
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Sear
ch
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ter
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s
o
f
s
p
ee
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d
ac
cu
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ac
y
[
2
5
]
.
I
t
is
co
m
p
u
tatio
n
ally
ef
f
icien
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a
n
d
s
u
itab
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tim
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ee
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ar
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eter
s
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n
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et
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s
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ch
as
g
e
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o
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ir
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e,
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O
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y
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o
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s
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ac
h
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licatio
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s
.
Fig
u
r
e
1
illu
s
tr
ates
th
e
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r
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ce
s
s
o
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d
etec
tin
g
AI
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g
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ated
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s
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ay
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s
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R
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o
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ized
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ata
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ata
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o
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ass
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elf
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ee
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e
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tu
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with
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O
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est
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s
AI
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ated
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,
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F1
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Fig
u
r
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1
.
R
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k
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2
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1
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Da
t
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T
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ataset
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ated
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les
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ased
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ain
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g
.
E
x
a
m
p
les in
cl
u
d
e:
a.
“
Ar
tific
ial
I
n
tellig
en
ce
is
tr
an
s
f
o
r
m
in
g
ed
u
ca
tio
n
b
y
en
ab
lin
g
p
er
s
o
n
alize
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lear
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in
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ex
p
e
r
ien
ce
s
.
”
b.
“
T
h
e
ca
p
ital o
f
Fra
n
ce
is
Par
is
.
”
Pre
p
r
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s
s
in
g
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v
o
lv
e
d
to
k
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i
za
tio
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s
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g
th
e
B
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T
to
k
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izer
,
f
o
llo
wed
b
y
p
ad
d
in
g
an
d
t
r
u
n
ca
tio
n
to
en
s
u
r
e
f
ix
ed
-
len
g
th
in
p
u
ts
.
T
h
e
p
r
o
ce
s
s
ed
to
k
en
s
wer
e
th
en
co
n
v
er
t
ed
in
to
Py
T
o
r
ch
ten
s
o
r
s
f
o
r
b
a
tch
p
r
o
ce
s
s
in
g
.
2
.
2
.
B
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RT
m
o
del a
rc
hite
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T
h
e
B
E
R
T
m
o
d
el
u
s
ed
in
th
is
r
esear
ch
is
b
ased
o
n
th
e
B
er
t
Fo
r
Seq
u
en
ce
C
lass
if
icatio
n
ar
ch
itectu
r
e.
T
h
e
m
o
d
el
in
clu
d
es
an
em
b
e
d
d
in
g
la
y
er
th
at
tr
a
n
s
f
o
r
m
s
i
n
p
u
t
to
k
en
s
in
to
v
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to
r
s
b
y
i
n
co
r
p
o
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atin
g
t
o
k
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em
b
ed
d
in
g
s
,
p
o
s
itio
n
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en
co
d
in
g
s
,
an
d
s
eg
m
en
t
em
b
e
d
d
in
g
s
.
T
h
ese
ar
e
f
o
llo
wed
b
y
1
2
tr
an
s
f
o
r
m
er
e
n
co
d
e
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
5
4
3
-
5
5
5
4
5546
lay
er
s
,
ea
ch
f
ea
tu
r
in
g
s
elf
-
atten
tio
n
m
ec
h
an
is
m
s
an
d
f
ee
d
-
f
o
r
war
d
n
etwo
r
k
s
.
E
ac
h
e
n
co
d
er
lay
er
ca
p
tu
r
es
b
id
ir
ec
tio
n
al
c
o
n
tex
tu
al
r
elatio
n
s
h
ip
s
am
o
n
g
th
e
to
k
e
n
s
.
T
h
e
m
o
d
el
en
d
s
with
a
p
o
o
lin
g
lay
er
b
ased
o
n
th
e
[
C
L
S
]
to
k
en
an
d
a
f
u
lly
co
n
n
ec
ted
class
if
icatio
n
lay
er
th
at
o
u
tp
u
ts
p
r
o
b
ab
ilit
ie
s
f
o
r
two
class
es:
h
u
m
an
-
wr
itten
o
r
AI
-
g
e
n
er
ate
d
.
I
n
to
tal,
th
e
ar
ch
itectu
r
e
c
o
n
tain
s
ar
o
u
n
d
1
0
9
.
5
m
illi
o
n
p
ar
am
eter
s
,
m
ak
in
g
it
h
ig
h
ly
e
x
p
r
ess
iv
e
an
d
ca
p
ab
le
o
f
h
an
d
lin
g
c
o
m
p
lex
te
x
t c
lass
if
icatio
n
task
s
.
2
.
3
.
P
SO
o
ptim
iza
t
i
o
n f
o
r
h
y
perpa
ra
m
et
er
t
un
ing
Hy
p
er
p
ar
a
m
eter
tu
n
in
g
is
c
r
itical
f
o
r
ac
h
iev
in
g
o
p
tim
al
p
e
r
f
o
r
m
a
n
ce
in
B
E
R
T
.
I
n
th
is
s
tu
d
y
,
we
u
tili
ze
PS
O
to
au
to
m
ate
t
h
e
tu
n
in
g
p
r
o
ce
s
s
f
o
r
th
r
ee
k
e
y
h
y
p
e
r
p
ar
am
ete
r
s
:
lear
n
in
g
r
a
te,
b
atch
s
ize,
a
n
d
n
u
m
b
er
o
f
tr
ain
in
g
ep
o
ch
s
.
PS
O
i
s
a
p
o
p
u
latio
n
-
b
ased
o
p
tim
izatio
n
alg
o
r
ith
m
in
s
p
ir
e
d
b
y
th
e
co
llectiv
e
m
o
v
em
en
t
b
eh
a
v
io
r
o
f
b
ir
d
s
o
r
f
is
h
.
E
ac
h
p
ar
ticle
in
th
e
s
w
ar
m
r
e
p
r
esen
ts
a
ca
n
d
id
ate
c
o
n
f
ig
u
r
atio
n
,
a
n
d
th
e
p
ar
ticles
iter
ativ
ely
u
p
d
ate
th
eir
p
o
s
itio
n
s
b
ased
o
n
th
eir
o
wn
b
est
p
er
f
o
r
m
an
ce
an
d
th
e
g
lo
b
al
b
est
co
n
f
ig
u
r
atio
n
[
2
6
]
.
T
h
e
ev
alu
atio
n
m
etr
ic
is
v
alid
atio
n
lo
s
s
,
co
m
p
u
ted
d
u
r
in
g
tr
ain
in
g
.
W
e
co
n
f
ig
u
r
e
PS
O
t
o
u
s
e
1
0
p
ar
ticles
an
d
a
m
ax
i
m
u
m
o
f
5
iter
atio
n
s
,
with
ea
r
ly
s
to
p
p
i
n
g
e
n
ab
led
to
p
r
ev
en
t
o
v
e
r
f
itti
n
g
.
T
h
is
ap
p
r
o
ac
h
s
ig
n
if
ican
tly
im
p
r
o
v
ed
th
e
m
o
d
el’
s
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e.
T
o
en
h
an
ce
t
h
e
r
ep
r
o
d
u
cib
ilit
y
o
f
o
u
r
ap
p
r
o
ac
h
,
we
p
r
o
v
i
d
e
a
clea
r
d
escr
ip
ti
o
n
o
f
th
e
in
te
g
r
atio
n
p
r
o
ce
s
s
b
etwe
en
B
E
R
T
an
d
PS
O.
T
h
e
in
p
u
t
ess
ay
s
f
ir
s
t
u
n
d
er
g
o
p
r
e
p
r
o
ce
s
s
in
g
an
d
to
k
en
izatio
n
u
s
in
g
t
h
e
B
E
R
T
to
k
en
izer
,
af
ter
w
h
ic
h
th
ey
ar
e
f
ed
in
to
th
e
B
er
tFo
r
Seq
u
en
ce
C
lass
if
icatio
n
m
o
d
el.
T
h
e
PS
O
alg
o
r
ith
m
in
itializ
es
a
p
o
p
u
latio
n
o
f
p
ar
ticles,
ea
ch
r
ep
r
esen
tin
g
a
p
o
s
s
ib
le
co
m
b
in
atio
n
o
f
h
y
p
e
r
p
ar
am
eter
s
s
u
ch
as
lear
n
in
g
r
ate,
b
atch
s
ize,
an
d
n
u
m
b
er
o
f
ep
o
c
h
s
.
Du
r
in
g
tr
ai
n
in
g
,
ea
ch
p
a
r
ticle'
s
co
n
f
ig
u
r
atio
n
is
ev
alu
ated
b
ased
o
n
v
ali
d
atio
n
lo
s
s
,
an
d
th
e
p
ar
ticles
u
p
d
ate
t
h
eir
p
o
s
itio
n
s
b
y
co
n
s
id
er
in
g
b
o
t
h
th
eir
o
wn
b
est
p
er
f
o
r
m
a
n
ce
an
d
th
e
g
lo
b
al
b
est
s
o
lu
tio
n
f
o
u
n
d
s
o
f
ar
.
T
h
is
p
r
o
ce
s
s
co
n
tin
u
es
iter
ativ
ely
u
n
til
a
n
o
p
tim
al
co
n
f
ig
u
r
atio
n
is
id
en
tifie
d
.
T
h
e
b
est
h
y
p
er
p
ar
am
eter
s
et
is
th
en
u
s
e
d
to
f
in
e
-
tu
n
e
th
e
B
E
R
T
m
o
d
e
l f
o
r
f
in
al
e
v
alu
atio
n
.
2
.
4
.
E
s
s
a
y
predict
io
n a
nd
ev
a
lua
t
io
n
T
h
e
tr
ain
e
d
an
d
o
p
tim
ized
B
E
R
T
m
o
d
el
is
th
e
n
u
s
ed
to
c
lass
if
y
ess
ay
tex
ts
as
h
u
m
an
-
wr
itten
o
r
AI
-
g
en
er
ated
.
Pre
d
ictio
n
s
ar
e
g
en
er
ated
b
y
f
ee
d
i
n
g
t
h
e
p
r
o
c
ess
ed
ess
ay
tex
t
in
to
th
e
f
in
e
-
tu
n
ed
m
o
d
el.
T
h
e
m
o
d
el
o
u
tp
u
ts
class
p
r
o
b
a
b
ilit
ies,
an
d
th
e
h
i
g
h
er
-
p
r
o
b
ab
ilit
y
class
is
s
elec
ted
as
th
e
p
r
ed
ictio
n
r
esu
lt.
T
o
f
ac
ilit
ate
p
r
ac
tical
u
s
ag
e,
th
e
m
o
d
el
is
d
ep
lo
y
e
d
u
s
in
g
a
Fas
tAPI
-
b
ased
web
in
ter
f
ac
e
.
User
s
ca
n
in
p
u
t
a
n
ess
ay
,
an
d
th
e
in
ter
f
ac
e
r
etu
r
n
s
an
im
m
ed
iate
p
r
ed
ictio
n
.
T
h
e
ef
f
ec
tiv
e
n
ess
o
f
th
is
p
r
e
d
ictio
n
p
r
o
ce
s
s
is
v
alid
ated
u
s
in
g
m
etr
ics
s
u
c
h
as
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e,
en
s
u
r
in
g
r
o
b
u
s
t
r
ea
l
-
tim
e
class
if
icatio
n
ca
p
ab
ilit
y
[
2
7
]
,
[
2
8
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Resul
t
o
f
t
o
k
eniza
t
io
n
T
h
e
to
k
e
n
izatio
n
p
h
ase
co
n
v
e
r
ts
th
e
in
p
u
t
tex
t
in
to
a
n
u
m
er
ical
r
ep
r
esen
tatio
n
o
p
tim
ized
f
o
r
B
E
R
T
m
o
d
el
p
r
o
ce
s
s
in
g
[
2
9
]
.
At
t
h
is
s
tag
e,
ea
ch
wo
r
d
is
s
eg
m
en
ted
in
to
to
k
en
s
,
eith
e
r
a
s
en
tire
wo
r
d
s
o
r
s
u
b
-
wo
r
d
s
,
in
alig
n
m
en
t
with
th
e
B
E
R
T
to
k
en
izer
'
s
s
tr
u
ctu
r
e.
T
h
is
s
eg
m
en
tatio
n
all
o
ws
u
n
co
m
m
o
n
o
r
co
m
p
lex
ter
m
s
to
b
e
b
r
o
k
en
d
o
wn
in
to
s
m
aller
u
n
its
,
th
er
eb
y
s
tr
en
g
th
en
in
g
th
e
m
o
d
el
’
s
ab
ilit
y
to
in
ter
p
r
et
an
d
g
e
n
er
alize
ac
r
o
s
s
d
iv
er
s
e
tex
tu
al
in
p
u
ts
.
Su
b
s
eq
u
en
tl
y
,
ea
ch
tex
t
is
s
tan
d
a
r
d
ized
to
a
f
ix
ed
len
g
th
u
s
in
g
p
ad
d
in
g
an
d
tr
u
n
ca
tio
n
b
ased
o
n
th
e
s
p
ec
i
f
ied
m
a
x
im
u
m
l
en
g
th
.
Pad
d
in
g
ad
d
s
b
lan
k
to
k
en
s
to
r
ea
ch
th
e
d
esire
d
len
g
t
h
,
w
h
ile
tr
u
n
ca
tio
n
s
h
o
r
ten
s
tex
ts
th
at
ex
ce
e
d
it.
T
h
is
s
tep
is
cr
u
cial
f
o
r
b
atch
p
r
o
ce
s
s
in
g
,
allo
win
g
th
e
m
o
d
el
to
r
ec
ei
v
e
in
p
u
ts
o
f
u
n
if
o
r
m
s
ize,
t
h
er
eb
y
s
tr
ea
m
lin
in
g
d
ata
p
r
o
ce
s
s
in
g
[
3
0
]
.
T
h
e
to
k
en
ized
o
u
t
p
u
t
is
s
u
b
s
eq
u
en
tly
tr
an
s
f
o
r
m
ed
in
to
Py
T
o
r
c
h
ten
s
o
r
s
,
allo
win
g
th
e
m
o
d
el
to
p
r
o
ce
s
s
th
e
in
p
u
t
d
ir
ec
tly
d
u
r
in
g
b
o
th
tr
ain
i
n
g
an
d
in
f
er
e
n
ce
.
B
y
s
tr
u
ctu
r
in
g
th
e
tex
t
in
ten
s
o
r
f
o
r
m
at,
it
is
p
r
ep
ar
ed
in
an
o
p
tim
ized
n
u
m
er
ical
f
o
r
m
f
o
r
m
o
d
el
co
m
p
u
tatio
n
,
wh
ich
e
n
h
an
ce
s
p
r
o
ce
s
s
in
g
s
p
ee
d
an
d
o
r
g
a
n
izatio
n
.
T
h
is
s
tep
en
s
u
r
es
th
at
th
e
te
x
t
is
co
n
s
is
ten
tly
an
d
e
f
f
icien
tly
r
ea
d
y
f
o
r
B
E
R
T
,
en
a
b
lin
g
th
e
m
o
d
el
to
c
o
n
ce
n
tr
ate
o
n
in
ter
p
r
etin
g
th
e
m
ea
n
in
g
an
d
s
tr
u
ctu
r
e
o
f
th
e
tex
t
f
o
r
d
esig
n
ated
NL
P
task
s
.
T
h
e
r
esu
l
ts
f
r
o
m
th
e
B
E
R
T
to
k
en
izer
ar
e
p
r
esen
ted
i
n
T
ab
le
1
.
T
ab
le
1
d
is
p
lay
s
th
e
r
esu
lts
o
f
to
k
en
izatio
n
,
p
r
o
v
id
in
g
th
e
n
u
m
er
ical
r
e
p
r
esen
tatio
n
o
f
t
h
e
o
r
ig
in
a
l
tex
t
p
r
ep
ar
ed
f
o
r
p
r
o
ce
s
s
in
g
b
y
th
e
B
E
R
T
m
o
d
el.
Du
r
in
g
th
is
p
r
o
ce
s
s
,
ea
ch
tex
t
s
eq
u
en
ce
s
tar
ts
with
th
e
s
p
ec
ial
to
k
en
[
C
L
S
]
,
i
n
d
icatin
g
th
e
b
eg
in
n
i
n
g
o
f
a
s
en
ten
ce
,
an
d
en
d
s
with
(
SEP
)
,
m
a
r
k
in
g
th
e
s
en
ten
ce
’
s
e
n
d
o
r
ac
tin
g
as
a
s
ep
ar
ato
r
b
etwe
en
d
if
f
er
en
t
tex
t
s
eg
m
en
ts
.
T
h
is
s
etu
p
h
elp
s
th
e
m
o
d
el
r
ec
o
g
n
ize
th
e
b
o
u
n
d
ar
ies
o
f
s
en
ten
ce
s
o
r
p
a
r
ag
r
a
p
h
s
.
E
ac
h
wo
r
d
o
r
p
ar
t
o
f
a
wo
r
d
is
co
n
v
e
r
ted
in
t
o
a
s
p
ec
if
ic
to
k
en
,
wh
er
e
co
m
p
lex
wo
r
d
s
o
r
th
o
s
e
n
o
t
f
o
u
n
d
in
t
h
e
v
o
ca
b
u
la
r
y
ar
e
b
r
o
k
en
d
o
w
n
in
to
s
m
aller
s
u
b
-
to
k
en
s
.
T
h
i
s
p
r
o
ce
s
s
g
en
er
ates
a
s
eq
u
en
ce
o
f
t
o
k
en
s
,
ea
c
h
g
i
v
en
a
u
n
iq
u
e
n
u
m
er
ic
I
D
with
in
B
E
R
T
's
v
o
ca
b
u
lar
y
,
s
u
ch
as
I
D
1
0
1
f
o
r
t
h
e
s
p
ec
ial
[
C
L
S
]
to
k
en
.
T
h
ese
I
Ds
allo
w
th
e
m
o
d
el
to
r
ec
o
g
n
ize
an
d
h
an
d
le
t
o
k
en
s
in
a
ca
lcu
lab
le
n
u
m
e
r
ic
f
o
r
m
at,
e
n
s
u
r
in
g
th
at
ea
c
h
w
o
r
d
o
r
s
u
b
-
wo
r
d
m
ain
tain
s
a
co
n
s
is
ten
t
r
ep
r
esen
tatio
n
d
u
r
in
g
c
o
m
p
u
tatio
n
al
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Op
timiz
in
g
p
a
r
a
mete
r
s
elec
tio
n
in
b
id
ir
ec
tio
n
a
l e
n
co
d
er
p
o
r
tr
a
ya
l fo
r
…
(
Teg
a
r
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r
ifin
P
r
a
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p
r
o
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s
s
es.
Mo
r
eo
v
er
,
to
ac
h
ie
v
e
co
n
s
is
ten
t
tex
t
len
g
t
h
,
p
ad
d
in
g
a
n
d
tr
u
n
c
atio
n
a
r
e
u
s
ed
.
E
x
tr
a
to
k
en
s
with
a
v
alu
e
o
f
0
ar
e
a
p
p
en
d
ed
to
s
h
o
r
ter
tex
ts
to
m
ee
t
th
e
d
esire
d
in
p
u
t
len
g
th
,
w
h
ile
tex
ts
ex
ce
e
d
in
g
th
e
m
ax
im
u
m
len
g
th
ar
e
tr
im
m
ed
ac
co
r
d
in
g
l
y
.
T
h
e
to
k
en
izatio
n
o
u
tp
u
t
also
p
r
o
v
id
es
an
atten
tio
n
m
ask
,
g
u
id
in
g
t
h
e
m
o
d
el
to
atten
d
to
r
elev
an
t
to
k
e
n
s
(
in
d
icate
d
b
y
a
v
alu
e
o
f
1
)
a
n
d
to
ig
n
o
r
e
p
a
d
d
in
g
to
k
en
s
(
in
d
i
ca
ted
b
y
a
v
alu
e
o
f
0
)
.
I
n
th
is
way
,
th
e
t
o
k
en
izati
o
n
p
r
o
ce
s
s
n
o
t
o
n
ly
tr
an
s
f
o
r
m
s
th
e
tex
t
in
to
a
n
u
m
er
ical
f
o
r
m
at
co
m
p
ati
b
le
with
B
E
R
T
b
u
t
also
en
h
an
ce
s
p
r
o
ce
s
s
in
g
ef
f
icien
cy
b
y
co
n
ce
n
t
r
atin
g
o
n
th
e
p
o
r
tio
n
s
o
f
tex
t
th
at
h
o
ld
r
elev
an
t
in
f
o
r
m
atio
n
.
T
ab
le
1
.
T
o
k
en
ized
tex
t r
ep
r
esen
tatio
n
s
O
r
i
g
i
n
a
l
t
e
x
t
To
k
e
n
s
To
k
e
n
I
D
s
A
t
t
e
n
t
i
o
n
“
C
a
r
s
.
C
a
r
s
h
a
v
e
b
e
e
n
a
r
o
u
n
d
s
i
n
c
e
t
h
e
y
b
e
c
a
m
e
a
v
a
i
l
a
b
l
e
t
o
t
h
e
p
u
b
l
i
c
.
.
.
”
[
C
LS]
,
c
a
r
s,
.
,
c
a
r
s,
h
a
v
e
,
b
e
e
n
,
a
r
o
u
n
d
,
si
n
c
e
,
t
h
e
y
,
b
e
c
a
me,
a
v
a
i
l
a
b
l
e
,
t
o
,
t
h
e
,
p
u
b
l
i
c
,
.
.
.
,
[
S
EP]
[
1
0
1
,
1
2
3
4
,
1
1
9
,
1
2
3
4
,
2
0
3
1
,
2
0
4
2
,
2
2
3
5
,
2
1
4
4
,
2
0
2
7
,
2
1
5
0
,
2
8
0
0
,
2
0
0
0
,
1
9
9
6
,
2
2
7
0
,
1
0
2
]
[
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
0
,
0
,
0
,
0
,
0
]
“
Tr
a
n
sp
o
r
t
a
t
i
o
n
i
s a
l
a
r
g
e
n
e
c
e
ssi
t
y
i
n
mo
s
t
c
o
u
n
t
r
i
e
s..
.
”
[
C
LS]
,
t
r
a
n
s
p
o
r
t
a
t
i
o
n
,
i
s,
a
,
l
a
r
g
e
,
n
e
c
e
ssi
t
y
,
i
n
,
m
o
st
,
c
o
u
n
t
r
i
e
s,
.
.
.
,
[
S
E
P
]
[
1
0
1
,
2
0
3
6
,
2
0
0
3
,
1
0
3
7
,
2
3
1
2
,
4
5
1
8
,
1
9
9
9
,
2
0
8
7
,
3
0
3
2
,
1
0
2
]
[
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
]
“
A
meric
a
's
l
o
v
e
a
f
f
a
i
r
w
i
t
h
i
t
s
v
e
h
i
c
l
e
s s
e
e
ms
e
t
e
r
n
a
l
.
.
.
”
[
C
LS]
,
A
mer
i
c
a
,
's
,
l
o
v
e
,
a
f
f
a
i
r
,
w
i
t
h
,
i
t
s,
v
e
h
i
c
l
e
s
,
se
e
ms,
e
t
e
r
n
a
l
,
.
.
.
,
[
S
EP]
[
1
0
1
,
2
6
3
7
,
1
5
2
1
,
1
0
5
5
,
2
2
9
3
,
3
9
4
1
,
2
0
0
7
,
2
0
4
9
,
5
8
7
2
,
4
0
2
3
,
1
0
2
]
[
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
]
“
H
o
w
o
f
t
e
n
d
o
y
o
u
r
i
d
e
i
n
a
c
a
r
?
D
o
y
o
u
d
r
i
v
e
a
c
a
r
o
f
t
e
n
?
”
[
C
LS]
,
h
o
w
,
o
f
t
e
n
,
d
o
,
y
o
u
,
r
i
d
e
,
i
n
,
a
,
c
a
r
,
?
,
d
o
,
y
o
u
,
d
r
i
v
e
,
a
,
c
a
r
,
o
f
t
e
n
,
?
,
[
S
EP]
[
1
0
1
,
2
1
2
9
,
2
4
1
1
,
2
0
7
9
,
2
0
1
7
,
3
8
5
4
,
1
9
9
9
,
1
0
3
7
,
2
4
8
2
,
1
0
2
9
,
2
0
7
9
,
2
0
1
7
,
3
2
9
8
,
1
0
3
7
,
2
4
8
2
,
2
4
1
1
,
1
0
2
9
,
1
0
2
]
[
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
0
,
0
]
…
…
…
…
“
C
a
r
s
a
r
e
a
w
o
n
d
e
r
f
u
l
t
h
i
n
g
.
Th
e
y
a
r
e
p
e
r
h
a
p
s
o
n
e
o
f
t
h
e
g
r
e
a
t
e
s
t
i
n
v
e
n
t
i
o
n
s
i
n
h
u
ma
n
h
i
s
t
o
r
y
.
”
[
C
LS]
,
c
a
r
s,
a
r
e
,
a
,
w
o
n
d
e
r
f
u
l
,
t
h
i
n
g
,
.
,
t
h
e
y
,
a
r
e
,
p
e
r
h
a
p
s,
o
n
e
,
o
f
,
t
h
e
,
g
r
e
a
t
e
st
,
i
n
v
e
n
t
i
o
n
s
,
i
n
,
h
u
m
a
n
,
h
i
s
t
o
r
y
,
.
,
[
S
EP
]
[
1
0
1
,
1
2
3
4
,
2
0
2
4
,
1
0
3
7
,
6
9
1
9
,
2
5
1
8
,
1
1
9
,
2
0
2
7
,
2
0
2
4
,
3
3
8
4
,
2
0
2
8
,
1
9
9
7
,
1
9
9
6
,
4
6
0
2
,
1
0
5
0
8
,
1
9
9
9
,
2
5
2
9
,
2
3
8
1
,
1
1
9
,
1
0
2
]
[
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
]
“
P
u
b
l
i
c
t
r
a
n
sp
o
r
t
a
t
i
o
n
o
f
f
e
r
s
man
y
b
e
n
e
f
i
t
s
t
o
u
r
b
a
n
r
e
si
d
e
n
t
s
b
y
r
e
d
u
c
i
n
g
t
r
a
f
f
i
c
a
n
d
p
o
l
l
u
t
i
o
n
l
e
v
e
l
s.
”
[
C
LS]
,
p
u
b
l
i
c
,
t
r
a
n
s
p
o
r
t
a
t
i
o
n
,
o
f
f
e
r
s,
man
y
,
b
e
n
e
f
i
t
s
,
t
o
,
u
r
b
a
n
,
r
e
s
i
d
e
n
t
s,
b
y
,
r
e
d
u
c
i
n
g
,
t
r
a
f
f
i
c
,
a
n
d
,
p
o
l
l
u
t
i
o
n
,
l
e
v
e
l
s
,
.
,
[
S
EP]
[
1
0
1
,
2
2
7
0
,
2
0
3
6
,
4
1
6
1
,
2
1
1
6
,
6
6
6
1
,
2
0
0
0
,
3
9
2
6
,
5
1
5
8
,
2
0
1
1
,
1
1
8
4
8
,
4
9
1
8
,
1
9
9
8
,
9
4
7
2
,
4
7
4
0
,
1
1
9
,
1
0
2
]
[
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
0
,
0
]
“
W
i
t
h
i
n
c
r
e
a
se
d
a
c
c
e
ss
t
o
p
u
b
l
i
c
t
r
a
n
s
p
o
r
t
a
t
i
o
n
,
m
o
r
e
p
e
o
p
l
e
c
a
n
r
e
l
y
l
e
ss
o
n
p
e
r
s
o
n
a
l
v
e
h
i
c
l
e
s.
”
[
C
LS]
,
w
i
t
h
,
i
n
c
r
e
a
s
e
d
,
a
c
c
e
ss,
t
o
,
p
u
b
l
i
c
,
t
r
a
n
s
p
o
r
t
a
t
i
o
n
,
,
,
mo
r
e
,
p
e
o
p
l
e
,
c
a
n
,
r
e
l
y
,
l
e
ss,
o
n
,
p
e
r
so
n
a
l
,
v
e
h
i
c
l
e
s,
.
,
[
S
EP]
[
1
0
1
,
2
0
0
7
,
4
1
2
2
,
3
2
2
9
,
2
0
0
0
,
2
2
7
0
,
2
0
3
6
,
1
1
7
,
2
0
6
2
,
2
1
1
1
,
2
0
6
4
,
4
8
0
1
,
2
6
2
9
,
2
0
0
6
,
3
1
6
0
,
5
8
7
2
,
1
1
9
,
1
0
2
]
[
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
1
,
0
,
0
,
0
]
3.
2
.
Resul
t
o
f
B
E
RT
a
rc
hite
ct
ure
T
ab
le
2
p
r
o
v
id
es
an
o
v
er
v
iew
o
f
th
e
B
E
R
T
m
o
d
el
ar
ch
itectu
r
e
tailo
r
ed
f
o
r
s
eq
u
en
ce
clas
s
if
icatio
n
,
co
n
tain
in
g
r
o
u
g
h
l
y
1
0
9
.
5
m
illi
o
n
p
ar
am
eter
s
in
to
tal.
T
h
e
e
m
b
ed
d
in
g
la
y
er
,
co
n
s
is
tin
g
o
f
wo
r
d
,
p
o
s
itio
n
,
an
d
to
k
en
-
ty
p
e
em
b
ed
d
in
g
s
,
in
c
lu
d
es
2
3
.
9
m
illi
o
n
p
a
r
am
et
er
s
an
d
co
n
v
er
ts
tex
t
in
p
u
t
in
to
v
ec
to
r
ized
r
ep
r
esen
tatio
n
s
.
At
th
e
m
o
d
el's
co
r
e,
1
2
en
co
d
er
lay
er
s
h
o
u
s
e
s
elf
-
atten
tio
n
m
ec
h
an
is
m
s
,
ea
ch
with
1
.
7
7
m
illi
o
n
p
ar
am
eter
s
p
er
lay
e
r
,
alo
n
g
with
f
u
r
t
h
er
tr
an
s
f
o
r
m
at
io
n
s
,
am
o
u
n
tin
g
to
ab
o
u
t
8
5
.
4
m
illi
o
n
p
ar
am
eter
s
in
to
tal.
Fo
llo
win
g
s
elf
-
atten
tio
n
,
ea
ch
en
c
o
d
er
lay
er
in
cl
u
d
es
a
d
en
s
e
lay
er
,
n
o
r
m
aliza
tio
n
,
an
d
d
r
o
p
o
u
t
to
en
h
an
ce
s
tab
ilit
y
.
An
in
ter
m
ed
iate
d
en
s
e
lay
er
tem
p
o
r
ar
il
y
ex
p
an
d
s
th
e
d
im
en
s
io
n
to
3
0
7
2
u
s
in
g
GE
L
U
ac
tiv
atio
n
(
r
eq
u
ir
i
n
g
2
.
3
6
m
illi
o
n
p
ar
am
eter
s
)
b
ef
o
r
e
r
etu
r
n
in
g
to
7
6
8
d
im
e
n
s
io
n
s
.
T
h
e
p
o
o
ler
lay
e
r
co
n
s
o
lid
ates
s
en
ten
ce
r
ep
r
esen
tatio
n
v
ia
th
e
[
C
L
S]
to
k
en
,
an
d
a
f
in
al
class
if
ier
lay
er
wi
th
1
,
5
3
8
p
ar
am
eter
s
g
en
er
ates th
e
class
p
r
ed
ictio
n
s
.
I
n
B
E
R
T
's
ar
ch
itectu
r
e,
ev
e
r
y
to
k
en
in
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tex
t
s
eq
u
e
n
ce
u
n
d
er
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o
es
m
u
ltip
le
tr
an
s
f
o
r
m
a
tio
n
s
tag
es,
en
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lin
g
th
e
m
o
d
el
to
u
n
d
er
s
t
an
d
b
o
t
h
th
e
co
n
te
x
t
an
d
th
e
co
m
p
lete
m
ea
n
in
g
o
f
th
e
s
en
ten
ce
[
3
1
]
.
T
ab
le
3
s
h
o
wca
s
es
th
e
B
E
R
T
p
r
o
ce
s
s
,
with
a
f
o
cu
s
o
n
th
e
em
b
ed
d
in
g
lay
e
r
o
u
tp
u
ts
,
s
elf
-
atten
t
io
n
o
p
er
atio
n
s
,
a
n
d
f
ee
d
-
f
o
r
war
d
n
etwo
r
k
(
FF
N)
s
tag
es
f
o
r
ea
ch
to
k
en
.
I
n
itially
,
ea
ch
to
k
en
p
ass
es
th
r
o
u
g
h
a
n
em
b
ed
d
i
n
g
s
tag
e,
wh
er
e
ea
ch
wo
r
d
o
r
s
u
b
wo
r
d
i
s
co
n
v
er
ted
in
to
a
7
6
8
-
d
im
en
s
io
n
al
v
ec
to
r
.
T
h
is
v
ec
to
r
m
er
g
es d
etails ab
o
u
t th
e
to
k
en
its
elf
,
its
p
o
s
itio
n
in
th
e
s
en
ten
ce
,
an
d
its
s
eg
m
en
t
o
r
i
g
in
.
T
h
is
em
b
e
d
d
ed
r
ep
r
esen
t
atio
n
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er
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as
th
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f
o
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n
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atio
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en
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a
p
r
elim
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tity
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ch
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r
d
w
ith
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th
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en
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s
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n
tex
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Fo
llo
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g
th
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b
e
d
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g
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ase,
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en
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o
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ag
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eter
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eg
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etwe
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s
p
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if
ic
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e
n
an
d
ev
er
y
o
t
h
er
to
k
en
with
i
n
th
e
s
en
ten
ce
.
T
h
is
m
ec
h
an
is
m
allo
ws ea
ch
to
k
en
to
“
f
o
cu
s
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r
d
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tex
t
u
ally
r
elev
an
t
o
r
cl
o
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ely
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n
n
ec
ted
to
it
[
3
2
]
.
As
an
ex
am
p
le,
in
th
e
p
h
r
ase
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C
ar
s
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av
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b
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ay
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r
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r
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lik
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“
h
av
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”
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b
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in
g
its
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n
d
e
r
s
tan
d
in
g
with
in
th
is
co
n
tex
t.
T
h
e
r
esu
lt
o
f
th
e
s
elf
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tio
n
s
tag
e
is
a
r
ich
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tex
t
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s
en
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itiv
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r
ep
r
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o
f
ea
ch
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e
n
,
in
f
o
r
m
e
d
b
y
its
r
elatio
n
s
h
ip
s
with
o
th
er
to
k
en
s
in
th
e
s
en
ten
ce
.
T
h
e
o
u
tp
u
t
f
r
o
m
s
elf
-
atten
tio
n
is
th
en
p
ass
ed
th
r
o
u
g
h
a
f
ee
d
-
f
o
r
war
d
n
etwo
r
k
(
FF
N)
,
a
lay
er
th
at
en
h
an
ce
s
th
e
u
n
d
er
s
tan
d
in
g
o
f
ea
ch
to
k
e
n
b
y
ap
p
ly
in
g
n
o
n
-
lin
ea
r
tr
an
s
f
o
r
m
ati
o
n
s
[
3
3
]
.
T
h
e
FF
N
en
h
an
ce
s
ea
ch
t
o
k
en
'
s
r
ep
r
esen
tatio
n
b
y
ad
d
i
n
g
lay
e
r
s
o
f
co
m
p
lex
ity
,
allo
win
g
id
e
n
tical
wo
r
d
s
to
r
e
f
lect
Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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Vo
l.
15
,
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6
,
Decem
b
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r
20
25
:
5
5
4
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5
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ip
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with
i
n
a
s
en
ten
ce
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T
ab
le
2
.
B
E
R
T
ar
ch
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r
e
d
e
tails
La
y
e
r
(
t
y
p
e
)
O
u
t
p
u
t
s
h
a
p
e
P
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r
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m#
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l
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f
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q
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l
e
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g
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h
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7
6
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.
5
M
(
t
o
t
a
l
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e
d
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l
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le
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.
B
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r
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s
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r
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To
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m
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t
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m
7
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F
N
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(
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3.
3
.
Resul
t
o
f
f
ine
-
t
un
ing
a
nd
hy
perpa
ra
m
et
er
o
ptim
iz
a
t
io
n
An
in
itial f
in
e
-
tu
n
in
g
o
f
th
e
B
E
R
T
m
o
d
el
is
co
n
d
u
cted
with
d
ef
au
lt p
ar
am
eter
s
to
estab
lis
h
a
r
eliab
le
b
aselin
e
b
ef
o
r
e
ap
p
l
y
in
g
t
h
e
PS
O
alg
o
r
ith
m
f
o
r
p
ar
am
eter
o
p
tim
izatio
n
.
T
ab
le
4
p
r
esen
ts
th
e
in
itial
p
er
f
o
r
m
an
ce
r
esu
lts
o
f
th
e
B
E
R
T
m
o
d
el
with
o
u
t
s
p
ec
if
ic
o
p
tim
izatio
n
,
s
er
v
in
g
as
a
r
ef
er
en
ce
f
o
r
ass
ess
in
g
p
er
f
o
r
m
an
ce
im
p
r
o
v
e
m
en
ts
af
ter
th
e
p
ar
am
ete
r
o
p
tim
izatio
n
p
r
o
ce
s
s
.
As
s
h
o
wn
in
T
ab
le
4
,
th
e
B
E
R
T
m
o
d
el
with
b
aselin
e
p
ar
a
m
eter
s
ac
h
iev
es
ac
cu
r
ac
y
lev
e
ls
b
etwe
en
8
3
%
an
d
9
4
%.
Alth
o
u
g
h
th
is
in
d
icate
s
r
ea
s
o
n
ab
ly
s
tr
o
n
g
p
er
f
o
r
m
an
ce
,
th
e
r
e
r
em
ai
n
s
p
o
ten
tial
f
o
r
f
u
r
th
e
r
en
h
an
ce
m
e
n
t
in
ev
alu
atio
n
m
etr
ics
lik
e
F1
-
s
co
r
e,
p
r
ec
is
io
n
,
a
n
d
r
ec
all
.
I
n
NL
P
m
o
d
elin
g
with
B
E
R
T
,
s
elec
tin
g
n
o
n
-
id
ea
l
p
ar
am
eter
co
m
b
i
n
atio
n
s
ca
n
lim
it
p
er
f
o
r
m
an
ce
,
p
ar
tic
u
lar
ly
i
n
s
o
p
h
is
ticated
task
s
lik
e
d
etec
tin
g
AI
-
g
en
e
r
ated
ess
ay
s
[
3
4
]
.
C
o
n
s
eq
u
en
tly
,
em
p
lo
y
i
n
g
p
ar
am
eter
o
p
tim
izatio
n
m
e
th
o
d
s
is
ess
en
tial
to
m
ax
im
ize
th
e
m
o
d
el’
s
ef
f
ec
tiv
en
ess
.
PS
O
is
ap
p
lied
f
o
r
h
y
p
e
r
p
ar
am
eter
tu
n
in
g
t
o
e
n
h
an
ce
t
h
e
B
E
R
T
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
in
d
etec
tin
g
AI
-
g
en
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ated
ess
ay
s
.
PS
O
i
s
a
m
etah
eu
r
is
tic
ap
p
r
o
ac
h
r
ec
o
g
n
ize
d
f
o
r
its
ef
f
ec
tiv
en
ess
in
o
p
tim
izin
g
p
ar
am
et
er
s
ac
r
o
s
s
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
lear
n
in
g
m
o
d
els,
in
clu
d
i
n
g
th
o
s
e
u
s
ed
i
n
NL
P.
PS
O
was
ch
o
s
en
d
u
e
to
its
ef
f
icien
cy
in
ex
p
l
o
r
in
g
th
e
p
ar
am
eter
s
p
ac
e
an
d
its
ef
f
ec
tiv
en
ess
in
u
n
co
v
er
i
n
g
o
p
tim
al
co
n
f
ig
u
r
atio
n
s
f
o
r
d
ee
p
lear
n
in
g
m
o
d
els
[
3
5
]
,
[
3
6
]
.
I
n
our
r
esear
ch
,
th
e
o
p
tim
i
za
tio
n
f
o
cu
s
es
o
n
th
r
ee
m
ai
n
h
y
p
er
p
ar
am
eter
s
ar
e
lear
n
in
g
r
ate,
b
atch
s
ize,
an
d
n
u
m
b
er
o
f
e
p
o
ch
s
to
m
i
n
im
ize
ev
alu
atio
n
lo
s
s
o
n
t
h
e
v
alid
a
tio
n
s
et.
T
h
e
s
ea
r
ch
b
o
u
n
d
ar
ies
ar
e
ca
r
ef
u
lly
d
ef
i
n
ed
with
in
a
n
ap
p
r
o
p
r
iate
r
a
n
g
e,
s
p
ec
if
y
in
g
th
e
lear
n
in
g
r
ate
b
etwe
en
1
×
10
−
6
an
d
1
×
10
−
4
,
b
atch
s
ize
is
co
n
s
tr
ain
ed
b
etwe
en
1
6
an
d
6
4
,
a
n
d
e
p
o
ch
s
ar
e
s
et
f
r
o
m
1
to
5
to
p
r
e
v
en
t
ex
tr
em
e
co
n
f
ig
u
r
atio
n
s
th
at
co
u
l
d
d
est
ab
ilize
th
e
tr
ain
i
n
g
p
r
o
ce
s
s
.
T
h
e
PS
O
o
b
jectiv
e
f
u
n
ctio
n
,
im
p
lem
en
ted
v
ia
th
e
T
r
ain
er
in
th
e
T
r
an
s
f
o
r
m
e
r
s
lib
r
ar
y
,
ass
ess
es
p
ar
am
eter
co
m
b
in
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n
s
b
ased
o
n
e
v
alu
atio
n
lo
s
s
.
Valid
atio
n
is
p
er
f
o
r
m
ed
ev
e
r
y
5
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n
d
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p
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tr
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if
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o
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ig
n
if
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ca
n
t
im
p
r
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v
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t
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er
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ac
r
o
s
s
th
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n
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ec
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tiv
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ev
alu
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.
PS
O
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g
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ate
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o
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ate
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r
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o
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atio
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l
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a
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n
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ab
le
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r
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tim
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ate
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ates
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B
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AI
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ated
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ay
d
etec
tio
n
task
s
.
T
ab
le
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ar
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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15
,
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5550
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ab
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ates
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ip
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atter
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ata.
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ter
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is
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ar
p
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ec
lin
e,
th
e
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ain
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lo
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r
em
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w
with
m
in
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f
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ct
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s
,
lik
el
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u
e
to
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atch
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a
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iatio
n
s
d
u
r
in
g
tr
ain
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.
Similar
ly
,
th
e
v
alid
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lo
s
s
also
d
r
o
p
s
s
wif
tly
ea
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ly
i
n
th
e
tr
ain
in
g
an
d
s
tab
ilizes
at
a
lo
w
le
v
el,
with
o
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ly
s
lig
h
t
f
lu
ctu
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n
s
.
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b
o
th
tr
ain
in
g
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d
v
alid
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s
s
lev
els
s
ta
b
ilize
with
o
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t
an
y
n
o
ticea
b
le
d
iv
er
g
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ce
,
th
er
e
is
n
o
in
d
ica
tio
n
o
f
o
v
e
r
f
itti
n
g
.
T
h
e
m
o
d
el
p
r
eser
v
es
its
ab
ilit
y
to
g
en
er
alize
to
u
n
s
ee
n
d
at
a,
as
r
ef
lecte
d
b
y
th
e
p
ar
allel
tr
en
d
s
i
n
b
o
th
l
o
s
s
cu
r
v
es.
T
h
is
r
elatio
n
s
h
ip
in
d
ic
ates
th
at
th
e
m
o
d
el
lear
n
s
ef
f
e
ctiv
ely
f
r
o
m
th
e
tr
ai
n
in
g
d
ata
wh
ile
m
ain
tain
in
g
its
ab
ilit
y
to
p
er
f
o
r
m
well
o
n
v
alid
atio
n
d
ata.
Fig
u
r
e
2
.
T
r
ain
in
g
a
n
d
v
alid
atio
n
lo
s
s
g
r
ap
h
3.
4
.
Resul
t
o
f
e
v
a
lua
t
io
n
I
n
p
u
r
s
u
it o
f
h
ig
h
er
d
etec
tio
n
ac
cu
r
ac
y
,
th
is
s
tu
d
y
n
o
t o
n
ly
f
o
cu
s
es o
n
d
ev
elo
p
in
g
an
ef
f
ec
tiv
e
m
o
d
el
b
u
t
also
in
teg
r
ates
it
in
to
a
web
-
b
ased
in
ter
f
ac
e.
T
h
is
d
ep
lo
y
m
en
t
allo
ws
u
s
er
s
to
p
er
f
o
r
m
r
ea
l
-
tim
e
d
etec
tio
n
o
f
AI
-
g
e
n
er
ated
ess
ay
s
,
as
d
e
m
o
n
s
tr
ated
in
Fig
u
r
e
3
,
e
n
h
an
cin
g
b
o
t
h
u
s
ab
ilit
y
an
d
ac
ce
s
s
ib
ilit
y
f
o
r
p
r
ac
tical
ap
p
licatio
n
s
.
T
h
e
d
ep
l
o
y
m
en
t
o
f
t
h
e
o
p
tim
ized
B
E
R
T
m
o
d
el
f
o
r
d
etec
tin
g
AI
-
g
en
er
ate
d
ess
ay
s
s
tar
ts
b
y
in
teg
r
atin
g
th
e
m
o
d
el
in
to
an
API
b
u
ilt
with
Fas
tAPI.
T
h
e
API
r
ec
eiv
es
ess
ay
tex
t
f
r
o
m
u
s
er
s
,
p
r
o
ce
s
s
es
it,
an
d
g
e
n
er
ates
a
p
r
ed
ictio
n
o
n
wh
eth
er
th
e
tex
t
was
au
th
o
r
ed
b
y
a
h
u
m
an
o
r
p
r
o
d
u
ce
d
b
y
AI
.
A
u
s
er
-
f
r
ien
d
ly
web
in
ter
f
ac
e
was
cr
ea
ted
,
in
clu
d
in
g
a
tex
t
in
p
u
t
f
ield
an
d
a
“
Pre
d
ict
”
b
u
tto
n
th
at
s
en
d
s
th
e
tex
t
to
th
e
API
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d
d
is
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lay
s
th
e
o
u
tco
m
e.
A
f
ter
d
ep
lo
y
m
e
n
t,
th
is
web
ap
p
licatio
n
allo
ws
u
s
er
s
to
in
p
u
t
ess
ay
tex
t
an
d
in
s
tan
tly
r
ec
eiv
e
d
etec
tio
n
r
esu
lts
,
d
eter
m
in
in
g
if
th
e
es
s
ay
was
lik
ely
g
e
n
er
ated
b
y
AI
,
as
s
h
o
wn
in
Fig
u
r
e
3
(
a
)
.
I
n
Fig
u
r
e
3
(
b
)
,
a
u
s
er
s
u
b
m
its
an
ess
ay
f
o
r
an
al
y
s
is
,
wh
ich
th
e
ap
p
licatio
n
p
r
o
ce
s
s
es,
r
etu
r
n
in
g
a
“
Stu
d
en
t
”
lab
el
s
u
g
g
esti
n
g
t
h
a
t th
e
tex
t w
as m
o
s
t lik
ely
wr
itten
b
y
a
h
u
m
a
n
.
T
h
e
d
e
p
lo
y
ed
m
o
d
el
en
a
b
les
th
o
r
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u
g
h
test
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g
o
n
a
d
ataset
o
f
9
,
2
5
0
ess
ay
s
,
p
r
ed
ictin
g
wh
eth
er
ea
ch
tex
t
was
g
en
er
ate
d
b
y
AI
o
r
w
r
itten
b
y
a
h
u
m
a
n
.
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n
a
d
d
itio
n
to
ac
c
u
r
ac
y
-
b
ased
e
v
alu
atio
n
,
we
also
m
ea
s
u
r
e
d
s
y
s
tem
p
er
f
o
r
m
an
ce
i
n
ter
m
s
o
f
r
esp
o
n
s
e
tim
e
a
n
d
lo
a
d
test
in
g
.
Du
r
in
g
r
ea
l
-
tim
e
p
r
ed
ictio
n
v
ia
th
e
Fas
tAPI
-
b
ased
in
ter
f
ac
e,
th
e
av
er
ag
e
r
esp
o
n
s
e
tim
e
was
r
ec
o
r
d
ed
at
3
1
2
m
illi
s
ec
o
n
d
s
p
er
r
eq
u
est,
with
a
m
ax
im
u
m
o
b
s
er
v
e
d
laten
c
y
o
f
4
8
7
m
illi
s
ec
o
n
d
s
u
n
d
er
n
o
r
m
al
co
n
d
itio
n
s
.
T
h
e
s
y
s
tem
was
f
u
r
t
h
er
test
ed
u
s
in
g
s
im
u
lated
co
n
c
u
r
r
en
t
u
s
er
r
eq
u
ests
(
lo
ad
test
with
5
0
0
r
e
q
u
ests
p
er
m
in
u
te
)
,
w
h
ich
s
h
o
wed
s
tab
le
p
er
f
o
r
m
an
ce
with
9
8
.
2
%
o
f
r
eq
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ests
co
m
p
leted
u
n
d
e
r
5
0
0
m
s
,
in
d
icatin
g
s
tr
o
n
g
s
ca
lab
ilit
y
f
o
r
m
o
d
e
r
ate
tr
af
f
ic
s
ce
n
ar
io
s
.
Fu
r
th
er
m
o
r
e,
in
f
o
r
m
al
u
s
er
f
ee
d
b
ac
k
w
as
co
llected
f
r
o
m
a
g
r
o
u
p
o
f
2
0
s
tu
d
e
n
ts
an
d
lectu
r
er
s
wh
o
u
s
ed
th
e
s
y
s
tem
f
o
r
ev
alu
atin
g
ess
ay
co
n
ten
t.
T
h
e
m
ajo
r
ity
(
8
5
%)
r
ep
o
r
ted
t
h
at
th
e
s
y
s
tem
was
in
tu
itiv
e
an
d
h
el
p
f
u
l,
esp
ec
ially
in
q
u
ick
ly
ass
ess
in
g
th
e
au
th
en
ticity
o
f
ac
a
d
em
ic
wr
i
tin
g
.
Sev
er
al
u
s
er
s
ap
p
r
ec
iated
th
e
clar
ity
o
f
th
e
p
r
ed
ictio
n
r
esu
lt,
th
o
u
g
h
a
f
ew
s
u
g
g
ested
th
e
ad
d
itio
n
o
f
m
o
r
e
d
etailed
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
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f
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ter
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n
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tim
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s
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c
o
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itio
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.
(
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u
r
e
3
.
W
eb
in
ter
f
ac
e
e
s
s
ay
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AI
d
etec
tio
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a)
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u
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n
d
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b
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r
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T
h
e
p
r
e
d
ictio
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en
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o
m
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ar
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to
t
h
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e
ls
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r
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ad
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itio
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er
f
o
r
m
an
ce
m
etr
ics,
p
r
o
v
i
d
in
g
a
clea
r
e
v
alu
atio
n
o
f
th
e
m
o
d
el'
s
ef
f
ec
tiv
en
ess
.
T
h
e
e
v
alu
atio
n
em
p
lo
y
s
k
ey
p
er
f
o
r
m
an
ce
m
etr
ics,
in
clu
d
i
n
g
ac
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r
ac
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,
p
r
ec
is
io
n
,
r
ec
all
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an
d
F1
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r
e,
to
r
i
g
o
r
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ly
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m
o
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el's
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f
ec
tiv
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ess
in
d
if
f
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tiatin
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AI
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g
en
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ate
d
ess
ay
s
f
r
o
m
h
u
m
an
-
a
u
th
o
r
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ts
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t
f
u
r
t
h
er
ex
am
in
es
th
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ap
p
licatio
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’
s
r
ea
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tim
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at
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icien
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al
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g
r
e
s
p
o
n
s
e
tim
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d
r
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r
o
s
s
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ied
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ay
ty
p
es
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e
n
s
u
r
e
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ep
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d
ab
le
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d
co
n
s
is
ten
t
p
er
f
o
r
m
an
ce
.
T
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F
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NC
E
S
[
1
]
N
.
T
y
a
g
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a
n
d
B
.
B
h
u
s
h
a
n
,
“
D
e
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i
f
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n
g
t
h
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o
l
e
o
f
n
a
t
u
r
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l
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a
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u
a
g
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p
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c
e
ssi
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(
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LP)
i
n
smar
t
c
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y
a
p
p
l
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c
a
t
i
o
n
s
:
b
a
c
k
g
r
o
u
n
d
,
mo
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o
n
,
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t
a
d
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c
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s
,
a
n
d
f
u
t
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r
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r
e
s
e
a
r
c
h
d
i
r
e
c
t
i
o
n
s,
”
W
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re
l
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ss
Pe
rso
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a
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C
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m
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c
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t
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n
s
,
v
o
l
.
1
3
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n
o
.
2
,
p
p
.
8
5
7
–
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0
8
,
2
0
2
3
.
[
2
]
O
.
A
l
i
,
P
.
A
.
M
u
r
r
a
y
,
M
.
M
o
mi
n
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Y
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K
.
D
w
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d
i
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a
n
d
T
.
M
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k
,
“
T
h
e
e
f
f
e
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t
s
o
f
a
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f
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c
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a
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g
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n
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e
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p
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t
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o
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s
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n
e
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u
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a
t
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o
n
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l
set
t
i
n
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s
:
C
h
a
l
l
e
n
g
e
s
a
n
d
s
t
r
a
t
e
g
i
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s
,
”
T
e
c
h
n
o
l
o
g
i
c
a
l
F
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s
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g
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n
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S
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l
C
h
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n
g
e
,
v
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l
.
1
9
9
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p
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1
2
3
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6
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F
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b
.
2
0
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4
,
d
o
i
:
1
0
.
1
0
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6
/
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.
t
e
c
h
f
o
r
e
.
2
0
2
3
.
1
2
3
0
7
6
.
[
3
]
F
.
K
a
ma
l
o
v
,
D
.
S
a
n
t
a
n
d
r
e
u
C
a
l
o
n
g
e
,
a
n
d
I
.
G
u
r
r
i
b
,
“
N
e
w
e
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a
o
f
a
r
t
i
f
i
c
i
a
l
i
n
t
e
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l
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g
e
n
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