NONPROFIT ORG. U.S. POSTAGE PAID May–June 2010 Chicago, IL Volume 44, Numbers 1–2 PERMIT #7706

50 East Washington Street Suite 500 Clearinghouse REVIEW Clearinghouse Chicago, Illinois 60602

Taking action to end poverty Volume 44, Numbers 1–2 Numbers Volume44, Asset Building

May–June 2010 May–June by People with Disabilities

Local Prosecution of Real-Estate Fraud

Rights of Limited-English-Proficient Individuals 1–92 Machine of Legal Information Toward Veterans Courts Language Access in State Courts The September–October 2010 Clearinghouse Review Mapping to Show Discrimination Unemployment Benefits Appeals Will be a Special Issue on Postracialism or Targeted Universalism? Climate Change and a Green Economy New Advocacy Opportunities How Effective Is of Legal Information?

By Michael Mulé and Claudia Johnson

Michael Mulé nline services offer instantaneous translation of text or website content. Staff Attorney Before using translation software or “machine-translation” tools to assist Empire Justice Center Olimited-English-proficient (LEP) clients obtain meaningful access to the 1 W. Main St. Suite 200 information, advocates should consider several factors.1 Rochester, NY 14614 585.295.5724 [email protected] Machine translation is the automatic translation of text from a source language into a target language without human intervention. Machine translation generally takes a dic-

Claudia Johnson tionary approach to language and does not factor in culture or custom. Here we describe Local Initiatives Coordinator the role of a human translator, discuss when and how to use machine translation in con- junction with human translation, and review available machine-translation services. Pro Bono Net 2414 Lariat Lane Richland, WA 99352 Human Translation 509.396.7934 [email protected] A translator converts written material from one or more source languages into a tar- get language. Translators must have excellent writing and analytical ability, knowl- edge of the vocabulary, context, and meaning of words in both languages, and ensure that the translated version of the text conveys with precision the idea and form of the original. A description of interpreting and translating as an occupation notes that “[t]ranslat- ing involves more than replacing a word with its equivalent in another language: sen- tences and ideas must be manipulated to flow with the same coherence as the source document so that the translation reads as though it originated in the target language.”2 This characteristic distinguishes human from machine translation, and it is why hu- man translation is usually the only option in the following circumstances:

1Meaningful access is attained when a translation accurately communicates to a limited-English-proficient individual the programs and services of an organization or agency (see U.S. Department of Justice, Guidance to Federal Financial Assistance Recipients Regarding Title VI Prohibition Against National Origin Discrimination Affecting Limited English Proficient Persons, 67 Fed. Reg. 41455, 41461 (June 18, 2002), http://bit.ly/bH3Jpe).

2U.S. Bureau of Labor Statistics, Interpreters and Translators, in Occ u p a t i o n a l Ou t l o o k Ha n d b o o k , 2010–11 Edition (last modified Dec. 17, 2009), http://bit.ly/9BWpez.

32 Clearinghouse REVIEW Journal of Poverty Law and Policy n May–June 2010 How Effective Is Machine Translation of Legal Information?

n Nuanced vocabulary—documents where be unacceptably inaccurate.”4 Before us- the source text or vocabulary is ambigu- ing machine translation for any text or a ous or unclear. Documents that have document, use the following suggestions text with nuanced meanings require the to improve accuracy and efficiency: skills of a translator who understands n the context and message behind the Most important, limit sentence length. words. Legal terminology is nuanced Sentences of more than twenty-five vocabulary since concepts do not have words often become ambiguous and too the same meaning in all legal systems. complex for machine-translation tools to translate correctly. Keeping sentenc- n Individualized documents—personal- es to no more than twenty words or so ized letters and documents with con- will improve the quality of the output. cepts and specialized terms. A human However, machine translation is not translator knows the vocabulary and very good at translating the names of can convey the ideas and intent of the legal documents because they include source text. legal jargon that may not be part of an- other legal culture. Short phrases such n Sufficient time—if the written text does as “motion for replevin,” “petition for not have to be translated immediately dissolution,” or “request for restitu- and no detriment to the client will re- tion” still require human translation to sult. Even if time is short, however, be incorporated accurately into a docu- use machine translation only for indi- ment, website, or form. vidual words or small phrases, not as a replacement for professional transla- n Avoid metaphors, jokes, slang, puns, tion. idiomatic expressions, and regional or national expressions. Since these are n Official —to ensure accu- often translated literally, they tend to racy when a certified or official trans- lose their meaning. The literal transla- lation of a document is necessary in a tion of “break a leg,” for example, will legal proceeding, government agen- not make sense to the target reader. cy, or application process.3 Machine translation cannot ensure the preci- n Avoid abbreviations, acronyms, and sion required for translation of vital contractions, which might not have documents that affect a client’s rights, equivalents in different languages. benefits, or services. Instead spell out the entire word. Machine-translation services do not Machine Translation always recognize abbreviations and may omit them from the translation. Unlike human translation, machine Instead of “Sr.,” “Jr.,” “DHS,” or “SSI,” translation merely replaces a word with use “senior,” “junior,” “department of its target language equivalent, without human services,” “supplemental secu- considering context or meaning. Trans- rity income.” lated words without context do not main- tain the idea or message of the original n Use simple, direct sentences with basic text. grammatical construction. Ensure that the sentence structure is grammatical- Agencies that rely on machine transla- ly correct and do not omit words (e.g., tion alone to ensure that LEP individu- “Make sure that you use grammatically als have meaningful access to document correct sentence structure” rather than and website content should know that “Make sure you use grammatically cor- machine translation has “been found to rect sentence structure”).

3See Fe d . R. Civ. P. 44(a)(2), http://bit.ly/9c4xpf.

4Federal Interagency Working Group on Limited English Proficiency, Top Tips from Responses to the Survey of Language Access Strategies Used by Federal Government Agencies (2008), http://bit.ly/chlup5.

Clearinghouse REVIEW Journal of Poverty Law and Policy n May–June 2010 33 How Effective Is Machine Translation of Legal Information?

n Avoid ambiguity. To produce a clear material for meaning to that target translation, minimize use of words and community. A larger group triggers sentences that have multiple meanings obligations under Title VI of the Civil (e.g., the word “right,” which can mean Rights Act of 1964 to ensure meaning- “correct” or “opposite of left”). ful access by translating vital docu- ments and thus requires a systemic n Avoid compound verbs, which are of- approach to implementing quality ten mistranslated. control in translations.7 n Use the international standard date n Importance of agency reputation or format (YYYY-MM-DD) when writing trust—because its results are not reli- dates, the format for which varies from ably accurate, using machine transla- country to country. Using the interna- tion can signal to the target group that tional standard format will help elimi- its needs are unimportant. nate translation problems. n Volume of material and degree of rep- n Because some languages do not use etition—a large quantity of material, the present participle verb form, use particularly if vocabulary and concepts the infinitive form instead (e.g., “click are repeated, is ideal for machine here to select the icons and to view the translation because programs usually images” rather than “click here for se- contain dictionaries that can be cus- lecting the icons and viewing the im- tomized and updated as needed. ages”). n Feasibility of human translation—if the n Give the translator a list of words that volume of material is extremely large, should remain in the source language time is short, and frequent updates will (e.g., proper names and titles and 5 be necessary, human translation may names of benefits and agencies). be unrealistic.

When to Use Machine Translation n Possibility of “gisting”—the practice of using machine translation to get a While machine translation cannot re- rough idea of the source text content is place human translation when quality called “gisting” (from “to get the gist and accuracy are crucial, it may be suit- of”); it can be effective and appropri- able for several forms of written content ate when an “official” translation is not or to supply an initial translation that a needed and to determine if a human human translator then reviews for accu- translation is necessary. racy and context.6 Take the following fac- tors into account before relying solely on n Extent of dissemination required— machine translation: when material must be translated from one into several other languages, ma- n Potential harm—the degree of poten- chine translation can be an adjunct to tial harm caused by a poor translation human translators, creating drafts for must be balanced against the need for human translators to edit instead of speed. requiring them to start from scratch. n Size of the target group—the larger the Since machine translation automati- cally maintains consistency of termi- target audience, the greater the need 8 for a human translator to review the nology, it saves translators time.

5Hzhang, Babel Not: Machine Translation for the Technical Communicator, Pr o Z.c o m , March 26, 2008, http://bit.ly/ ao8DgO.

6A good model policy that incorporates some of these considerations for the translation of website content, with a focus on providing meaningful access, was developed by the Texas Health and Human Services System (see Texas Health and Human Services System, Accessibility Procedures (n.d.), http://bit.ly/b1DEYG).

7Limited English Proficiency: A Federal Interagency Website, Questions and Answers, (n.d.), http://bit.ly/br044c (Q. When developing plans and guidance regarding translation of documents, how do we determine which documents must be translated?).

8Hzhang, supra note 5; Kirti Vashee, The Continuing Evolution of Automated Translation Technology: RbMT vs. SMT, Pr o Z. c o m , Jan. 16, 2010, http://bit.ly/cW4USa.

34 Clearinghouse REVIEW Journal of Poverty Law and Policy n May–June 2010 How Effective Is Machine Translation of Legal Information?

Online Machine-Translation Services n Systran, http://www.systransoft.com/, creates software that instantly trans- Machine-translation tools, services, and lates text from and into fifty-two lan- software are available. Each has the in- guages. herent limitations described above but can be used to create drafts that human n Apertium, http://www.apertium.org, is translators review and finalize. These a rule-based, open-source machine- services are often free and easy to use and translation engine that focuses mostly allow agencies and programs to take the on Latin-based European languages. first step toward complying with their ob- n ligations under Title VI. Babelfish, http://babelfish.yahoo.com/, is similar to and is A trained translator who has expertise in powered by Systran rule-based machine both the source legal language and that of translation. the target group should evaluate the out- n put from machine-translation tools. If the Google Translator Toolkit, http:// translator determines that the machine translate.google.com/toolkit/, allows output is not meaningful, then the mate- human translators to work faster and rial must be edited. Therefore, depending more accurately, aided by technologies on the length and nature of the material, such as Google Translate, which cre- professional human translation from the ates “automatic translations” produced outset may be more cost-effective than purely by technology, without human the two-step process required by machine intervention. Google Translator Tool- translation. kit allows users to upload HTML (hy- pertext markup language), Microsoft Google Translate and Systran are ex- Word, OpenDocument Text, Rich Text, amples of services that allow for quick and Plain Text documents for transla- statistical translations of text and sen- tion. Alternatively the address of an tences. Google Translate does a statistical individual Web page can be entered for analysis by searching for similar already- translation of that page. translated sentences and then suggest- n ing the most probable equivalent. It does POOTLE, http://translate.sourceforge. not search for meaning or similarity of net/wiki/pootle/index, is a Web portal meaning.9 Some services focus on a spe- that allows online translation, collab- cific language while others cover multiple orative translation, and volunteer con- languages.10 These are some of the avail- tribution. able online translation services and their n Google Transliteration, http://www. features: google.com/transliterate/, allows trans- n Google Translate, http://translate. lation of words into languages that use non-Roman alphabets. At this writing google.com, is a “statistical machine 12 translation system, which means that it supports twenty-two languages. It it doesn’t try to unpick or understand also has tools that can be added to web- anything.” 11 Rather it looks for similar sites; one such tool is Virtual Keyboard, sentences in already translated texts on an onscreen keyboard that allows LEP the Internet. Google Translate is free website users to type in any of over and covers fifty-two languages. seventy-five languages by using their familiar keyboard layouts.13

9See David Bellos, I, Translator, Ne w Yo r k Ti m e s , Mar. 20, 2010, http://nyti.ms/dkEAeb.

10See Word2Word Language Resources, Machine Translation Software (n.d.), http://bit.ly/94n1Xa.

11Bellos, supra note 9.

12Google Transliteration Labs, Google Transliteration (2009), http://bit.ly/96RsRZ. The languages are Amharic, , Bengali, Greek, Gujarati, Hebrew, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Persian, Punjabi, Russian, Sanskrit, Serbian, Sinhalese, Tamil, Telugu, Tigrinya, and .

13Google Code, AJAX Language API for Transliteration (2010), http://bit.ly/cIcXTn; id., AJAX Language API for Virtual Keyboard (2010), http://bit.ly/cO6Vix.

Clearinghouse REVIEW Journal of Poverty Law and Policy n May–June 2010 35 How Effective Is Machine Translation of Legal Information?

How to Use Translation Tools and Public interest law offices and agencies Next Steps should pool resources to meet the trans- lation needs of their LEP clients.14 In the While many of the machine-translation face of limited resources, explore the services offer a variety of features, they possibility of creating online networks alone cannot replace the unique skills of translators who can revise and fine- of a human translator or be solely re- tune machine translations of vital docu- lied upon to give LEP clients meaning- ment forms and website content. This ful access to information and services. approach has been successful in some The better approach is to use machine- communities and can serve as a model translation services to create a draft that for public interest law offices and agen- a human translator then reviews for ac- cies working to improve access to our curacy and to capture the idea and form justice system.15 of the original.

14Julia Alanen, Language Access Is an Empowerment Right: Deprivation of Plenary Language Access Engenders an Array of Grave Rights Violations, 1 International Le g a l St u d i e s Pr o g r a m La w Jo u r n a l 98 (2009), http://bit.ly/b1ApQz.

15See Global Voices, Lingua Global Voices Translation Project (n.d.), http://bit.ly/doax4V; Technology, Entertainment, Design, TED Open Translation Project (n.d.), http://bit.ly/axFYch.

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