taus mt showcase, creating competitive advantage with rapid customization & deployment of moses,...

TAUS MACHINE TRANSLATION SHOWCASE

Creating Competitive Advantage with Rapid Customization & Deployment of Moses 10:20 – 10:30 Thursday, 10 October 2013 Tony O’Dowd KantanMT

No Hardware. No So,ware. No Hassle MT.

Tony O’Dowd Founder & Chief Architect

Localiza6on World 2013

TAUS – MT Showcase

What we aim to cover today? �  User Scenario #1

�  Building Produc?on MT Systems �  Structured Approach �  Build – Measure – Learn Process

�  User Scenario #2 �  Retraining with Post-‐Edits

�  RoundTable Inc. – their story

�  User Scenario #3 �  Selec?ng the best engine for the job

�  Milengo – their approach �  GeLng the Translator involved

�  Q&A

20 Minutes


What is KantanMT.com? �  Sta6s6cal MT System

�  Cloud-‐based �  Highly scalable �  Inexpensive to operate �  Quick to deploy

� Our Vision �  To put Machine Transla?on

�  Customiza?on �  Improvement �  Deployment

�  into your hands

Ac6ve KantanMT Engines

6,632 Training Words Uploaded

23,653,605,925 Member Words Translated

362,291,925

Fully Opera?onal 7 months


Measure – KantanMT engine calibra?on

�  Track using KantanWatch™ �  Compare engines quickly �  Monitor produc?on data �  Use your own test/tune data sets


Learn – KantanMT Experimenta?on



� What to look out for? BLEU F-‐Measure TER Wordcount

24% 50% 66% 172K



�  Learn from examining the output

§  Catalogue Errors §  Untranslated text §  Incorrect numeric

formaLng §  Invalid characters §  High level of post-‐edi?ng

required

§  Conclusions

§  Engine coverage is bad due to low wordcount

§  Post-‐Edi?ng is high due to low engine coverage

§  Training data doesn’t contain correct numeric formaLng

§  Bad formaLng in training data

Low OK High Low



§  Ac6on Plan §  Coverage – More training

data required, relevant and of high quality. Also use a Glossary File to improve terminology consistency and accuracy.

§  Numeric Forma_ng – Use PEX rule to post-‐edit transla?on and fix numeric formats

§  Invalid Character – Use PEX rule to fix this invalid character issue

§  Post-‐Edi6ng – By increasing the quan?ty of training data the KantanMT engine will perform be]er overall

Low OK High Low

�  Learn from examining the output


Ac6on Plan – focus on improving measurements


Build Measure Learn : The Results � Analyse output

§  Untranslated text §  Numeric FormaLng §  Invalid Character


User Scenario #2 �  Long history of MT usage

�  In-‐house exper?se �  Large customer demand �  Using MT since 2005 �  Now manage their own in-‐house system on the KantanMT.com

�  Goal �  Faster project turnaround ?mes �  More service offerings to client base �  More produc?on capacity �  Cost efficiencies

About RoundTable Studio RoundTable Studio is a leading provider of transla?on and localiza?on services for the Spanish and Brazilian Portuguese language markets.

Early Adopter


User Scenario #2 �  Business Scenario

�  Con?nuous transla?on quality improvement �  Reduced post-‐edi?ng/turn-‐around ?mes

Early Adopter


User Scenario #2 � Results

�  Greater produc?on capacity �  Improvement in quality �  Faster project turn-‐around ?mes

Early Adopter

“Since signing up with KantanMT, we have been able to take on more work and increase our capacity levels”

Laura Grossi – MT Specialist, RoundTable Studio


User Scenario #3 �  Long history of MT usage

�  In-‐house exper?se �  Large customer demand

�  Originally outsourced MT �  3rd party consultancy company

�  Vendor Agnos6c �  Microso, Translator Hub �  KantanMT.com

�  All systems are cloud based �  Like hands-‐on approach to managing their own MT engines

About Milengo Milengo provides transla?on, localiza?on and related language services specializing in so,ware, website and documenta?on localiza?on.


User Scenario #3 �  Business Scenario

�  Select best engine for language combina?on

�  Client requests a job that involves a MT component �  Finding Training Data

�  Data is aggregated from the clients previous transla?ons

�  Building Engines �  Same training data is provided to each engine �  Same language combina?ons �  Itera?ve process un?l sa?sfied with system performance (internal process)


User Scenario #3 �  Transla6on Quality Analysis

�  Sample of 1,000 segments selected �  Tabulated & anonymised

�  Dispatched to Senior Translators

Source MT Target

Adequ

acy (Score 1-‐5)

Fluency (Score 1-‐5)

Overall q

uality (1-‐4)

Wrong term

inology

Wrong Spellin

g

Source not

Transla

ted/Omissions

Compliance with

client sp

ecs

Literal transla

tion

Text/Information added

Capitalization

Wrong W

ord Form

Wrong Part o

f Speech

Punctuation

Sentence Structure

Tags and

Markup

Locale Adaptation

Spacing

Style Syntax and Grammar Tech


User Scenario #3 �  Feedback collated from Senior Translators

� Match best engine for language quality �  Very unique – pseudo-‐crowd sourcing of most appropriate engine

� Match engine to best language support �  Translators always involved in engine selec?on process

�  Feedback to client � Match requirements and quality expecta?ons


User Scenario #3 �  Levels of post-‐edi6ng services

�  Adequacy Review �  All meaning expressed in the source segment appears in

the translated segment �  Structural integrity – tags, placeholders �  Fit-‐for-‐purpose quality

�  Fluency Review �  No grammar errors, excellent word selec?on and good

syntax �  Publishable quality

�  Client picks review �  To fit budget, ?me-‐frame, audience, channel etc.

Tony O’Dowd [email protected]

taus mt showcase, creating competitive advantage with rapid customization & deployment of moses,...

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