2013 GALA Miami: Breaking into Latin Maerican Markets on a Small Budget
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This document discusses an MT case study of localizing Spanish translations for different Latin American countries on a small budget. It describes translating from English to standard Spanish, then using MT to adapt the Spanish to different Latin American variants like Argentine Spanish, Mexican Spanish, etc. Testing showed the prototype engine reduced errors from 7.78% to 1.21% compared to human localization. While not perfect, combining human and machine translation helped achieve high quality at a lower cost. Further work would focus on improving glossaries and expanding language coverage.
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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