1.
Wednesday,
4
June
Sovee
Smart
Engine
2.0:
A
Leap
Beyond
Base
Moses
Technology
Sco$
Gaskill,
Sovee
TAUS
Machine
TranslaDon
Showcase
2014
Dublin
(Ireland)
The
research
within
the
project
MosesCore
leading
to
these
results
has
received
funding
from
the
European
Union
7th
Framework
Programme,
grant
agreement
no
288487

2.
Presented by: Scott Gaskill
Christopher Klapp
June 4, 2014
MT
Showcase

3.
3
I
skate
to
where
the
puck
is
going
to
be,
not
where
it
has
been.
Wayne
Gretzsky,
Hockey
Star

4.
4
Where
is
the
world
going?
CNNTech,
“Google
boss:
EnDre
world
will
be
online
by
2020,”
April
2013
hXp://www.cnn.com/2013/04/15/tech/web/eric-­‐schmidt-­‐internet
Kenya
stat
from
ITU,
2-­‐13.
Photo
used
by
permission
of
Deseret
News.
2016
the
world
will
have
internet
connecDvity
By
the
end
of
this
decade
everyone
in
the
world
will
be
on
the
Web,
with
Mobile
access
growing
as
the
preferred
interface
In
Kenya,
99%
of
Internet
connecDons
are
mobile

5.
5
We
are
entering
the
Convergence
era:
translaDon
will
be
a
uDlity
embedded
in
every
app,
device
and
screen.
Businesses
will
prosper
by
finding
new
customers
in
new
markets….
Consumers
will
become
world-­‐wise,
communicaDng
as
if
language
barriers
never
existed.
Jaap
van
der
Meer,
Director
of
TAUS,
2013

6.
6
Transla9on
Memory
–
Is
More
Be?er?
If
we
simply
add
an
addiDonal
1,000
TM
lines
to
a
database
of
40-­‐60
billion,
will
we
see
beXer
translaDons?
Knowing
how
to
use
the
data
is
key

7.
7
Challenges
Technology,
approach
&
process
Progress
in
first
60
years
Progress
Needed
by
2016
Engines
for
<
150
Languages
Engines
for
>
6000
languages
<
3%
of
the
world’s
content
translated
All
content
translated
Cloud-­‐based
speed
providing
more
servers
for
translaDon
92
billion
Servers
StaDsDcal
translaDon
introduced,
but
“fuzzy
logic”
does
not
deliver
quality
businesses
need
Quality
improvement
to
standards
required
to
meet
world
commerce
demand

8.
8
4
(
n(n-­‐1)
2
)
Generic
SMT
92
million
9.2
billion
–
based
on
100
businesses
92
billion
Based
on
1000
customers
Not
valued
as
pracDcal
–
infinite
servers
required
MT
Assets
(cascades)
Technology
Challenge
6800 languages
Generic
SMT
Domain
Generic
SMT
Domain
Customer
Generic
SMT
Domain
Customer
Project
Minimum
Server
Requirements

9.
9
Accuracy
Challenge
Relevant
Segments
General
Corpus
Adequacy
Accuracy
General
MT
(30-­‐40%)
TM
(40-­‐60%)
Post
EdiDng
(up
to
100%)

10.
Preparing
new
project
/
import
TM
/
CAT
Leverage
Exact
Fuzzy
Match
Post
Edit
Review
Deliver
to
customer
Gather
past
TM
Package
and
send
TM
to
SMT
provider
Clean,
tokenize,
data
(prepare
data)
Train
–Tune-­‐Test
(3Ts)
Repeat
unDl
viewed
as
acceptable
(repeat
with
customer
data
each
Dme)
10
Post
Edi9ng
Learning
Engine
SMT
Workflow
Segments
are
not
just
a
string
of
text
–
they
are
a
living
learning
en99es
Process
Real-time Automation and Integration
Sovee
Smart
Engine
2.0

11.
11
Smart
Engine
Advantages
Language
from
Scratch
Seamless
integra9on
to
Post
Edi9ng
workflow
Training
/
Learning
Efficiency
Gains
(what
we
have
seen)
Post
ediDng
–
50%+
improvement
TM
/MT
management
and
training
–
100%
improvement
Update
MT
on
the
fly
Watch
it
learn
before
your
eyes
Never
leave
the
post
ediDng
environment

12.
12
Learned
Transla9ons
!"#"$%&'()"*+"&',( -"&".%#((/0.12,((
34"52%67(
3662.%67(
-"&".%#(89(
:0+%;&(
(<.*%&;=%>0&(
/2,'0+".(
?.0@"6'(
3,,"'(
9%*,(
Cascading
Assets
Sovee
Smart
Engine
MT
Learned
Segments
Segment
output

13.
1
2
Asset
Synchrony
(CAT
Tools)
Post
edi9ng
interface
Smart
Engine
13
Asset
Push
(Past
TM)
Real-­‐9me
progressive
transla9on
cycle
(Sovee
MT,
save
/push
post
edits)
1
2

14.
14
Demo

15.
15
Seamless
Integra9on
“Convergence
Era”
Apps
Websites
eCommerce
elearning
Videos
Podcasts
Sorware
Live
chat
Text
Messages
email

16.
Japan
Sovee
Smart
Engine
TranslaDon
USA
Yukiko
(Japan):
ホールインワンを決めたよ！
Robert
(USA):
I
just
scored
a
hole-­‐in-­‐one!
Original:
ホールインワンを決めたよ！
Japan
SNAG

17.
17
Jack
Nicklaus
Learning
Leagues
Languages:
Spanish
and
Japanese
In
Process:
10
more
languages
Video
and
Training
Materials
for
Golf
Instruc9on

18.
18
R.E.
Michel

19.
19
Ques9ons?