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An MT Case Study:
Breaking into Latin American Markets
on a Small Budget
María Azqueta (SeproTec) & Diego Bartolomé (tauyou)
Spanish Worldwide
Spanish Language:
• Also known as Castellano.
• Latin-derived Romance language.
• Spanish is one of the six official languages of
the United Nations and an official language of
the European Union.
Spanish Worldwide
Spanish Worldwide
0 200 400 600 800 1000 1200
Mandarin Chinese
Spanish
English
Hindi/Urdu
407 million
311 million
955 million
360 million
Second most spoken language by number of native speakers
Spanish Worldwide
• For demographic reasons, the percentage of the
orld’s populatio that speaks Spa ish as a ati e
language is increasing, while the percentage of
Chinese and English speakers is decreasing.
• Withi three or four ge eratio s, % of the orld’s
population will communicate in Spanish.
• I 5 , the U ited States ill e the orld’s
foremost Spanish speaking country.
Spanish on the Internet
• Spanish is the third most widely used language on
the Net.
• The use of Spanish on the Net has experienced a
growth rate of 807.4% between 2000 and 2011.
• Spain and Mexico are among the 20 countries with
the highest number of internet users.
• The demand for documents in Spanish is the fourth
largest fro a o g the orld’s la guages.
Spanish Worldwide and its Differences
High demand for translations into Spanish.
But… is the same Spanish spoken
everywhere?
Spanish Worldwide and its Differences
RAE (Royal Spanish Academy) :
– Created in the 18th century, it is widely seen as
the arbiter of what is considered standard
Spanish.
– It produces authoritative dictionaries and
grammar guides.
– Although its decisions are not formally binding,
they are widely followed in both Spain and Latin
America.
Spanish Worldwide and its Differences
Lexical
variations
Grammatical
differences
Idioms
Different dialects and many differences:
Spanish Worldwide and its Differences
‘Neutral’ or
‘International’
Spanish
Latin American
Spanish &
European
Spanish
Market Trend:
Why Adapt to the
Local Spanish of Each Country?
To reach different markets
People are most likely to buy when a product is
advertised in their dialect
Why Adapt to the
Local Spanish of Each Country?
EN: Take a card from the deck
ES: Coge una carta de la baraja
Client A (Gaming Industry)
Why Adapt to the
Local Spanish of Each Country?
ES: Coge una carta de la baraja
AR: Agarrá una carta del mazo
CL: Toma una carta del naipe
CO: Coge una carta de la baraja
MX: Saca una carta de la baraja
PR: Coge una carta de la baraja
Coger (32 entries)
http://rae.es/rae.html
1.tr. Asir, agarrar o tomar. U. t. c. prnl.
31. intr. vulg. Am. Realizar el acto sexual
Why Adapt to the
Local Spanish of Each Country?
2013 GALA Miami: Breaking into Latin Maerican Markets on a Small Budget
Advise Clients
If you really want to break into a specific
market, you must decide which country
you want to target and localize your
material for the different Spanish dialects
spoken in each individual country.
The Main Problems Clients Face
Is there a cost-efficient solution
on the market?
tauyou MT Solution at SeproTec
Hybrid machine translation since January 2011
La guages: EN, ES, PT, GA, FR, IT…
Do ai s: Legal, Te h i al…
Glossaries and forbidden words lists
Average translated words per month: 700,000
Initial Brainstorming
MT from
EN > different ES dialects
Extensive post-editing
would be required
Final Scope of the Project
Human translation + revision
English > Spanish (Spain)
MT of Spanish (Spain) into
Spanish from:
• Argentina
• Chile
• Colombia
• Mexico
• Puerto Rico
Initial Approach for Latin American MT
Traditional Workflow
. Gather tra slatio e ories (EN → ES-XX)
2. Add generic material
3. Develop engine
4. Add linguistic pre- and post-processing
5. Improve quality over time
Drawbacks
Varying MT Quality
Depending on the domain and dialect
Initial Inconsistencies among Dialects
Handled with glossaries
Medium Post-Editing Effort
Could be improved over time
New Approach
Translate EN to Standard ES
Via standard high-quality human translation
Convert Standard ES to Latin American Variants
From Spanish to Spanish
Better final quality is achieved
Specifications
Countries
Argentina, Chile, Colombia, Mexico, Puerto Rico
Internal Glossaries to Handle Lexical Variations
It corrects discordance
Idioms
Grammatical Differences
It adapts verb tenses
Testing the Prototype Engine
Extraction of several texts (fashion, real-
estate, human resources, automobile)
Sent to linguists and/or translators in
each target country for localization
Performance of the same localizations
by the engine
Comparison and contrasting of human
and machine localization results
First Bug Report
Not all terms
were localized
Concordance
issues
(masc./fem.;
sing./pl.)
Verbal tenses
for Argentina
Human vs. Machine
MT: 7.78 % error rate
First Bug Report
Some terms were changed/localized by the
engine, but not by the humans.
(example)
Human error or MT error?
Testing the Prototype Engine
A glossary was created by
extracting the terms localized by the
linguists/translators.
This glossary was then sent to
the same people who localized
the texts to verify that all the
terms were correctly localized
and nothing was missing.
Testing the Prototype Engine
The glossary grew by 36.91%!
Testing the Prototype Engine
People can miss things.
Although many different variants of Spanish
exist, Spanish speakers understand many
terms that are foreign to their own dialect
when they read them in context,
sometimes to the point of accepting them
as their own. I believe that this may be
due to the phenomenon of globalization
and the internet.
Latest Bug Report
MT: 1.21% error rate
Achievements
Very little post-editing needed
Reduced error rate
Shortened deadlines
Significant cost reduction
Conclusions
Human localization is not perfect.
MT is not perfect either.
Combining human and machine translation
helps achieve high quality and reduce cost.
Further Work
Improving Glossaries
Through a simple web interface for PE
Extending Spanish Language Coverage
More dialects
Traductor.cervantes.es
Incorporating more languages
English, French and Portuguese
Bibliography
Yule, G. (2006). The Study of Language: Third
Edition, Cambridge University New York.
RAE
Instituto Cervantes
http://www.linguapress.com
THANK YOU FOR
YOUR TIME!
María Azqueta
mazqueta@seprotec.com
Diego Bartolomé
diego.bartolome@tauyou.com

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2013 GALA Miami: Breaking into Latin Maerican Markets on a Small Budget

  • 1. An MT Case Study: Breaking into Latin American Markets on a Small Budget María Azqueta (SeproTec) & Diego Bartolomé (tauyou)
  • 2. Spanish Worldwide Spanish Language: • Also known as Castellano. • Latin-derived Romance language. • Spanish is one of the six official languages of the United Nations and an official language of the European Union.
  • 4. Spanish Worldwide 0 200 400 600 800 1000 1200 Mandarin Chinese Spanish English Hindi/Urdu 407 million 311 million 955 million 360 million Second most spoken language by number of native speakers
  • 5. Spanish Worldwide • For demographic reasons, the percentage of the orld’s populatio that speaks Spa ish as a ati e language is increasing, while the percentage of Chinese and English speakers is decreasing. • Withi three or four ge eratio s, % of the orld’s population will communicate in Spanish. • I 5 , the U ited States ill e the orld’s foremost Spanish speaking country.
  • 6. Spanish on the Internet • Spanish is the third most widely used language on the Net. • The use of Spanish on the Net has experienced a growth rate of 807.4% between 2000 and 2011. • Spain and Mexico are among the 20 countries with the highest number of internet users. • The demand for documents in Spanish is the fourth largest fro a o g the orld’s la guages.
  • 7. Spanish Worldwide and its Differences High demand for translations into Spanish. But… is the same Spanish spoken everywhere?
  • 8. Spanish Worldwide and its Differences RAE (Royal Spanish Academy) : – Created in the 18th century, it is widely seen as the arbiter of what is considered standard Spanish. – It produces authoritative dictionaries and grammar guides. – Although its decisions are not formally binding, they are widely followed in both Spain and Latin America.
  • 9. Spanish Worldwide and its Differences Lexical variations Grammatical differences Idioms Different dialects and many differences:
  • 10. Spanish Worldwide and its Differences ‘Neutral’ or ‘International’ Spanish Latin American Spanish & European Spanish Market Trend:
  • 11. Why Adapt to the Local Spanish of Each Country? To reach different markets People are most likely to buy when a product is advertised in their dialect
  • 12. Why Adapt to the Local Spanish of Each Country? EN: Take a card from the deck ES: Coge una carta de la baraja Client A (Gaming Industry)
  • 13. Why Adapt to the Local Spanish of Each Country? ES: Coge una carta de la baraja AR: Agarrá una carta del mazo CL: Toma una carta del naipe CO: Coge una carta de la baraja MX: Saca una carta de la baraja PR: Coge una carta de la baraja
  • 14. Coger (32 entries) http://rae.es/rae.html 1.tr. Asir, agarrar o tomar. U. t. c. prnl. 31. intr. vulg. Am. Realizar el acto sexual Why Adapt to the Local Spanish of Each Country?
  • 16. Advise Clients If you really want to break into a specific market, you must decide which country you want to target and localize your material for the different Spanish dialects spoken in each individual country.
  • 17. The Main Problems Clients Face
  • 18. Is there a cost-efficient solution on the market?
  • 19. tauyou MT Solution at SeproTec Hybrid machine translation since January 2011 La guages: EN, ES, PT, GA, FR, IT… Do ai s: Legal, Te h i al… Glossaries and forbidden words lists Average translated words per month: 700,000
  • 20. Initial Brainstorming MT from EN > different ES dialects Extensive post-editing would be required
  • 21. Final Scope of the Project Human translation + revision English > Spanish (Spain) MT of Spanish (Spain) into Spanish from: • Argentina • Chile • Colombia • Mexico • Puerto Rico
  • 22. Initial Approach for Latin American MT Traditional Workflow . Gather tra slatio e ories (EN → ES-XX) 2. Add generic material 3. Develop engine 4. Add linguistic pre- and post-processing 5. Improve quality over time
  • 23. Drawbacks Varying MT Quality Depending on the domain and dialect Initial Inconsistencies among Dialects Handled with glossaries Medium Post-Editing Effort Could be improved over time
  • 24. New Approach Translate EN to Standard ES Via standard high-quality human translation Convert Standard ES to Latin American Variants From Spanish to Spanish Better final quality is achieved
  • 25. Specifications Countries Argentina, Chile, Colombia, Mexico, Puerto Rico Internal Glossaries to Handle Lexical Variations It corrects discordance Idioms Grammatical Differences It adapts verb tenses
  • 26. Testing the Prototype Engine Extraction of several texts (fashion, real- estate, human resources, automobile) Sent to linguists and/or translators in each target country for localization Performance of the same localizations by the engine Comparison and contrasting of human and machine localization results
  • 27. First Bug Report Not all terms were localized Concordance issues (masc./fem.; sing./pl.) Verbal tenses for Argentina Human vs. Machine MT: 7.78 % error rate
  • 28. First Bug Report Some terms were changed/localized by the engine, but not by the humans. (example) Human error or MT error?
  • 29. Testing the Prototype Engine A glossary was created by extracting the terms localized by the linguists/translators. This glossary was then sent to the same people who localized the texts to verify that all the terms were correctly localized and nothing was missing.
  • 30. Testing the Prototype Engine The glossary grew by 36.91%!
  • 31. Testing the Prototype Engine People can miss things. Although many different variants of Spanish exist, Spanish speakers understand many terms that are foreign to their own dialect when they read them in context, sometimes to the point of accepting them as their own. I believe that this may be due to the phenomenon of globalization and the internet.
  • 32. Latest Bug Report MT: 1.21% error rate
  • 33. Achievements Very little post-editing needed Reduced error rate Shortened deadlines Significant cost reduction
  • 34. Conclusions Human localization is not perfect. MT is not perfect either. Combining human and machine translation helps achieve high quality and reduce cost.
  • 35. Further Work Improving Glossaries Through a simple web interface for PE Extending Spanish Language Coverage More dialects Traductor.cervantes.es Incorporating more languages English, French and Portuguese
  • 36. Bibliography Yule, G. (2006). The Study of Language: Third Edition, Cambridge University New York. RAE Instituto Cervantes http://www.linguapress.com
  • 37. THANK YOU FOR YOUR TIME! María Azqueta mazqueta@seprotec.com Diego Bartolomé diego.bartolome@tauyou.com