Localization Technology for Business Decision Makers

David Filip, Moravia WorldwideAngelika Zerfaß, zaac

Localization World Berlin, 2010

The myths• [hand out glossary]• Technology will solve all our problems• Translation Tools/Technologies– TM (Translation Memory)• Never translate (and pay for) a sentence twice

– TMX (Translation Memory Exchange)• Moving data from one TM tool to another without

problems

– Machine Translation (MT)• Can be used wherever you need it

The myths

• Terminology– It's just words, right?

• Content Management (CMS)– Send out only new or changed text to translation

More • Cloud and Crowd• ROI• TCO

Technology will solve all our problems

• Like buying a car does not solve the problem that you need to learn to drive, read a map (use the navigation system), know where to add petrol, oil, water… buying a translation tool does not give you a good/fast/cheap translation

• Same as with a car, localization technology will require continuous investment of effort and money after you have purchased it

Technology will solve all our problems

• Technology helps us to work faster and more efficiently– But the technology itself does not solve the problem, so

investing in a tool is only one step

• You need to Adapt your text creation processes to the translation tool Train your authors, translators, project managers, reviewers Documentation of file preparation / post-translation processes Connect the tool with authoring systems, CMS, project

management systems, in-house/home-grown systems…

Technology will solve all our problems

• Documentation of existing processes before buying a tool and investing in a technology• Test where the software/technology can help improve

your processes• Test where you need to adapt your processes to be able

to use the tools efficiently• Price comparison comes at the end, when you know

what you want and what each tool can offer and how each tool would meet your requirements

• Your technology simply needs to be aligned with your core business needs

TM – never translate (and pay for) the same sentence twice

– Translation Memory Systems help achieve better consistency, faster turnaround and can save money

– Whenever the same sentence that already has been translated before has to be translated again, the TM system SUGGESTS the existing translation to the translator (so-called 100% match)

– The translator has to» Check if the translation does not contain any mistakes (TMs

save what you give them, they don't check for spelling mistakes or the like)

» Does the translation make sense in this context» Does the translation (length) fit the available space» Is the terminology used correctly

Examples

• The sentence needs to be reviewed– Sentence = penalty or phrase?

• If there is not enough volume, go to step 4.– Volume = sound or mass/size?

• The client does not have the necessary access rights.– Client = client computer or customer?

Context Information

• Saving context in the TM helps to improve matches

• Context information– Structural information (segment is a heading, content of a

table cell…– Surrounding information (previous and following segments)– IDs of strings from software files

• Translator still has to check for terminology, typos, appropriateness in context

Advanced Leverage

• Re-use of smaller parts from a translation memory than whole segments– Advanced Leverage aka sub-segment matching

• Becomes particularly important because of actual lack of segmentation rules standardization and importance of legacy TM assets– Different tools create different segments because they

use different rules to recognize segments

Standard File formatsMoving Data between Tools

• Standards like TMX for exchanging the contents of a TM system with another TM system and XLIFF as a standard format for translating any file format, help to make it easier to move between different tools

• But these standard formats can still be different from tool vendor to vendor as the specification allows different "flavors“, other tools can often only read the text informationo Formatting information might get lost in TMX Exchangeo Customized information might get lost or can only be

preserved partly in TMX exchangeo Specific features of XLIFF might not be supported

TMX– Tool A• Segmentation rule: Colon is not a segment end character

– "Please consider: There should be more than one."– Tool A sees one segment

– Tool B• Segmentation rule: Colon is a segment end character

– "Please consider:"– "There should be more than one."– Tool B sees two segments

– After moving TM data from Tool A to Tool B, there will be no match for the 2 separate sentences during translation

TMX

• Using TMX to move translation memory data from one tool to the next is possible– Allows to use different translation tools within one

project

– Will result in loss of match rates between 5 and 20% depending on the file format that was used to create the original TM and the rules for segmentation of each tool

Interoperability

– Translation/Localization is not an isolated process, tools need to interface with• Authoring environment, document/content management,

project management, web resources

– Increasing demand for interoperability on client side leads to better and smarter implementation of standards such as XML, XLIFF…• Some tools can e.g. adapt segmentation rules to make better

leverage of legacy material• Sub-segment matching and context matching and metadata

can also help a lot

MT – fast, use it everywhere

– Machine translation is getting used more and more, but works best on text that has been written for the machine (controlled language)• Post-editing is always necessary if the translation needs to be of good

quality• MT system needs to be constantly trained and configured to prevent

mistakes from occurring over and over again

– Feeding MT systems with sentence pairs from TMs is a good idea – if the quality of the TM content is proven and controlled

MT

• Microsoft Support Pages

ROI of MT• The sweet-spot for MT just is NOT the traditional

content– The ROI of MT on traditional content is boring

• Even good TM can be dirty from the MT-training point of view

• There is not a “vanilla” MT – the purpose determines everything

• The sweet-spot is customer support and community-generated content

Mismatch case-study• L10N manager wants to shorten turnaround times

and save on per-word rate… Localized release comes 10 weeks after source

• Is it an MT-ready scenario?

• We need to ask more diagnostic questions

• 6% new content, structured authoring

• The solution is not MT! Process optimization, in-context and sub-segment leverage

• But MT is buzzword-compliant

Types of MT• General SMT (Statistical MT) or RBMT (Rule-

based MT)– Low level of investment– Gisting, perishable content like e-mail or IM

• RBMT with highly specifically customized glossaries, domain specific SMT– Medium level of investment– Productivity post-editing in maximum few dozens

of percent gross savings

Types of MT• Highly specifically customized hybrid systems

(usually SMT or tree-based/example-based)– Highest level of investment and high cost of

ongoing maintenance

– Draft quality – good enough in certain publication contexts – such as support or knowledge base articles, community-generated content, etc.

Terminology – it's just words, right?

• Terminology defines the corporate identity– Company specific and subject matter specific

• Terminology questions / misunderstandings by translators (and later by clients) are costly

• What needs to be done Source terminology first

– Allowed / forbidden terms for authors to check Target terminologies

– Correct usage and forbidden terms can be checked Source and target language style guides

CMS – Send out only new or changed text to translation

• Content Management Systems store text modules that can be re-used during authoring

• Changed or new modules can be extracted for translation

Single modules (often only single sentences) are harder to translate than full chapters, because of context

Single modules, sent to translation, might later not conform to the style of the existing modules, if no comparison is possible by the translator

Structuring your data into modules is a significant effort and requires some rethinking by the authors as well

Structured authoring and translation

• Is a great idea

• However, you need to combine it with other ideas to succeed

• Be sure to bring the context to the translator– Previews, embedded or referenced screenshots– Guaranteed matches from previous translations should

be included to provide context, even though locked or otherwise excluded from translation…

Business case• Technology decisions are not too much different

compared to other business decisions

• Executive sponsors should ask their techies to explain how technology of a particular kind will improve their competitiveness in core business areas

• Avoid chasing for buzzword compliance, ask for ROI

Cloud and Crowd• Extremely fashionable buzzwords equal extreme

danger of overkill and mismatch– Putting a piece of old-fashioned software onto a data

center somewhere on the West Coast hardly solves your issues. Even if it is considered cloud and hence cool

– Platforms that really scale and can use the cloud flexibility are not yet mature

– Architecture issue with systems that need centrally valid data

Cloud and Crowd• Other types of possible mismatch

– You might not need the flexibility of expenditure– Your current solution can easily scale for your needs

mid-term– You might need tight database-level integrations with

systems running in-house– You might not have the community that is needed for a

successful crowdsourcing project

TCO• Factors

– Longevity? Critical application? Complex existing infra?

• Interoperability, Customizability– APIs, SDKs, open standards, open source, licensing of

non-production instances, escrow

• Avoid chasing for buzzword compliance, ask for TCO– You do not need to be bleeding edge outside of your

core business area

Take-aways

• Business Case– Require human readable explanations

• Avoid overkill– Make sure that you are using the full potential of your

solutions– Do not buy cool features that you won’t be able to

exploit for the life time of the tool

• Avoid mismatch

Thank you very much for your attention