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What your sales team really needs to know
about MT (but is afraid to ask?)

John Tinsley
CEO and Co-founder
GALA New York. Brooklyn. 22nd March 2016
This is a piece
of string
Asking how MT works is like asking…how long is
a piece of string?
Machine Translation is a complex sell
But it doesn’t have to be that hard!
Speaking from a position of knowledge
Machine Translation with Subject Matter Expertise
 Understand the client’s project in detail
 Assess your capability to deliver the project
 Work closely with your MT partner
3 take home messages
Why MT?
•  Speed
•  Cost savings
•  Time to market
•  Your competitors are
doing it!
“Why would I need MT?”
Why Now?
•  volume of content is
growing
•  demand, more words less
time
•  growth facilitator
•  #FOMO – you’re missing
out on business
What’s the MT value proposition?
“What type of MT is it?”
statistical
rule based
hybrid
syntactic
custom
hierarchical
composite
cloud
DIY
intelligent
enterprise
•  The goal of the MT here is to be good enough so that - on
the whole – with TMs, translators are faster post-editing some
segments
•  Challenges
–  development has to focus on reducing needs for edits, not
necessarily anything else
–  translator acceptance always a big barrier
–  evaluation can take time and has many factors
–  pricing models
How will your clients use MT?
What are the use cases for MT?
Translator productivity through post-editing
•  The goal is to produce MT that’s fit for a particular purpose as is
•  Arguably easier from an MT development perspective
•  Often high-volumes = more achievable
How will your clients use MT?
What are the use cases for MT?
MT for information
COST
 FEASIBILITY
The MT Butterfly Effect
Language
Volumes
Content type
Training data
Integration requirements
 Quality requirements
TM leverage
MT experience
Language
Not all languages are created equal
French
 German
 Turkish
 Finnish
Spanish
 Chinese
 Korean
 Hungarian
Portuguese
 Japanese
 Thai
 Basque
Volume
Not all languages are created equal
The more words…the better…the worse?
MT experience
Little experience A lot of experience
Hard
“Easy”
LSP/vendor
experience with MT
Ease of
adoption
The more experience the LSP has
with onboarding/training vendors,
and the more experience the vendor
has with MT, the more feasible the
adoption of MT will be
Quality requirements
•  Fully automatic human quality
•  300% post-editing productivity
•  French to Spanish == English to Korean
•  Best performance out of the box
TM Leverage
High TM
Leverage
Low MT
Effectiveness
Matches
 # words
Context 403,803
100% 585,459
95-99% 50,366
85-94% 41,604
75-84% 32,319
50-74% 18,972
No Match 81,119
Total 1,213,643
Only 8% of
all words go
to MT
Integration requirements
Standard vs Custom Integration

“instant” solution costs rise
proportionality with the number of
languages and the throughput
needs
Training Data
Corpora. Dictionaries. Terminology.
Content Type
The bed was two twin beds put together and
me and my girlfriend kept fallin in the middle
(since we like to cuddle) and that was iritating
Late nite room service was awesome
Social Media
User Generated Content
Highly Technical
Marketing, Nuanced
The Conundrum
Confront the elephant in the room!
•  “What volume of words do you estimate for the project?”
•  “Do we have translation memories, glossaries that are
relevant? Can we create them?”
•  “If so, what leverage are we getting?”
•  “To we have post-editors? Access to a supply chain?”
–  “what experience do they have?”
•  “Where will MT fit in the workflow (depending on the use
case)?”
•  “What variety is there in the content that the MT will be
processing?”
•  “Why aren’t you using Google Translate?”
•  “Is there sufficient budget for this project?”
What questions should YOU be asking?
 Understand the client’s project in detail
 Assess your capability to deliver the project
 Work closely with your MT partner
3 take home messages
“How much
training data do I
need?”
“How frequently
can I retrain the
engine?”
“What
happens to my
data?”
“Do you do
language X?”
“How good is
the quality?”
“How do you
measure
performance
over time?”

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What your sales team really needs to know about MT (but is afraid to ask?)

  • 1. What your sales team really needs to know about MT (but is afraid to ask?) John Tinsley CEO and Co-founder GALA New York. Brooklyn. 22nd March 2016
  • 2. This is a piece of string Asking how MT works is like asking…how long is a piece of string?
  • 3. Machine Translation is a complex sell
  • 4. But it doesn’t have to be that hard!
  • 5. Speaking from a position of knowledge Machine Translation with Subject Matter Expertise
  • 6.  Understand the client’s project in detail  Assess your capability to deliver the project  Work closely with your MT partner 3 take home messages
  • 7. Why MT? •  Speed •  Cost savings •  Time to market •  Your competitors are doing it! “Why would I need MT?” Why Now? •  volume of content is growing •  demand, more words less time •  growth facilitator •  #FOMO – you’re missing out on business What’s the MT value proposition?
  • 8. “What type of MT is it?” statistical rule based hybrid syntactic custom hierarchical composite cloud DIY intelligent enterprise
  • 9. •  The goal of the MT here is to be good enough so that - on the whole – with TMs, translators are faster post-editing some segments •  Challenges –  development has to focus on reducing needs for edits, not necessarily anything else –  translator acceptance always a big barrier –  evaluation can take time and has many factors –  pricing models How will your clients use MT? What are the use cases for MT? Translator productivity through post-editing
  • 10. •  The goal is to produce MT that’s fit for a particular purpose as is •  Arguably easier from an MT development perspective •  Often high-volumes = more achievable How will your clients use MT? What are the use cases for MT? MT for information
  • 11.
  • 12. COST FEASIBILITY The MT Butterfly Effect Language Volumes Content type Training data Integration requirements Quality requirements TM leverage MT experience
  • 13. Language Not all languages are created equal French German Turkish Finnish Spanish Chinese Korean Hungarian Portuguese Japanese Thai Basque
  • 14. Volume Not all languages are created equal The more words…the better…the worse?
  • 15. MT experience Little experience A lot of experience Hard “Easy” LSP/vendor experience with MT Ease of adoption The more experience the LSP has with onboarding/training vendors, and the more experience the vendor has with MT, the more feasible the adoption of MT will be
  • 16. Quality requirements •  Fully automatic human quality •  300% post-editing productivity •  French to Spanish == English to Korean •  Best performance out of the box
  • 17. TM Leverage High TM Leverage Low MT Effectiveness Matches # words Context 403,803 100% 585,459 95-99% 50,366 85-94% 41,604 75-84% 32,319 50-74% 18,972 No Match 81,119 Total 1,213,643 Only 8% of all words go to MT
  • 18. Integration requirements Standard vs Custom Integration “instant” solution costs rise proportionality with the number of languages and the throughput needs
  • 20. Content Type The bed was two twin beds put together and me and my girlfriend kept fallin in the middle (since we like to cuddle) and that was iritating Late nite room service was awesome Social Media User Generated Content Highly Technical Marketing, Nuanced
  • 21. The Conundrum Confront the elephant in the room!
  • 22. •  “What volume of words do you estimate for the project?” •  “Do we have translation memories, glossaries that are relevant? Can we create them?” •  “If so, what leverage are we getting?” •  “To we have post-editors? Access to a supply chain?” –  “what experience do they have?” •  “Where will MT fit in the workflow (depending on the use case)?” •  “What variety is there in the content that the MT will be processing?” •  “Why aren’t you using Google Translate?” •  “Is there sufficient budget for this project?” What questions should YOU be asking?
  • 23.  Understand the client’s project in detail  Assess your capability to deliver the project  Work closely with your MT partner 3 take home messages
  • 24. “How much training data do I need?” “How frequently can I retrain the engine?” “What happens to my data?” “Do you do language X?” “How good is the quality?” “How do you measure performance over time?”