bibusiness sis trategies for b ildib uilding …...title microsoft powerpoint - asia online - lrc-...

B i S i f B ildiBusiness Strategies for Building Strategic Advantage and Revenue

from Machine Translation

Dion WigginsChief Executive OfficerChief xecutive [email protected]

Copyright © 2011, Asia Online Pte LtdCopyright © 2011, Asia Online Pte Ltd

Cloud is one of many delivery models, not a quality model


Near HumanNew techniques mature

Hybrid MT Platforms

Businesses start to consider MTLSPs start to adopt MT

New skills develop in editing MTGood Enough Threshold

Google drivesProcessors became powerful enoughLarge volumes of digital data available

Gist

New skills develop in editing MT

Early SMTVendors

Good Enough Threshold

MT acceptance

Qua

lity

g g

BabelfishGoogle switches

from Systran to SMT

911 ‐> Research fundingQuality plateau as RBMT reached its limits in many languages – only marginal improvement.y g g y g p

Experimental


Early RBMT improved rapidly as new techniques were discovered.

CorporateCorporate Brochures

Product Brochures

2,000

10 000

Example WordsHuman

User Interface

Products Product Brochures

Software Products

M l / O li H l

10,000

50,000

200 000

Enterprise Information

User Documentation Manuals / Online Help

HR / Training / Reports

200,000

500,000

Existing Markets $31.4B

New Markets

Support / Knowledge Base

Communications 10,000,000

20,000,000+

Email / IM

Call Center / Help Desk

User Generated Content 50,000,000+Blogs / ReviewsMachine

Problem: Only 0.5% of what needs to be translated today is being translated due to cost and time constraints.

Solution: Humans cost too much and take too long. The average human translates at 2,000‐3,000 words/day.Near‐human quality machine translation offers an alternative to human that is faster and lower cost and can deliver huge volumes of content quickly – millions of words/day. M hi T l i (MT) li i d h f Wh bi d


Machine Translation (MT) quality is now good enough for many new purposes. When combined with human post editing MT can deliver gold standard quality in a fraction of the time.Many business cases previously not viable due to time and cost constraints now make sense.

Wait for a Project That Requires MTWait for a Project That Requires MT

Create a Product For Resale

Create a Product For Resale

• Opportunistic approach• Many LSPs are interested in MT, but not

• Proactive approach• Leverages existing translation assets• Can be sold to many clients

That Requires MTThat Requires MT For ResaleFor Resale

willing to take the plunge without a paying client.

• Limited to one clientH d t ll l l l

• Can be sold to many clients• Easier to sell ‐ test and show• Can sell multiple language pairs at the same

time• Harder to sell – longer sales cycle• Often build one language pair to try, before

committing to others

• Generally a higher Return On Investment (ROI)

Revenue RevenueRecurring revenues from words translatedOne time revenues from resale of customization

Recurring revenues from words translatedPreparing source dataOne time revenues from resale of customization

Post editingPreparing source dataTerminology definition

Preparing source dataPost editingRuntime glossary preparationNon‐translatable terms definition


Non‐Translatable termsUnknown and high‐frequency phrase resolution

Reduce Project Costs Reduce Project Costs Faster Translation Delivery

Faster Translation DeliveryDelivery Delivery

• Helps to manage margin squeeze:– compete with competitors using cheaper (perhaps

lower quality) resources or competitors using MT

• New projects that could not have been delivered on due to time and resource constraintsq y) p g

• Helps to cost justify business cases that may not be viable using a human only approach

• Can be used behind the scenes (like a

constraints• Helps clients that want to simultaneously

ship product in multiple languages• New clients in research, analysis, data

translation memory) or disclosed to client• More client work in other areas as a result of

left over translation budget.

ymining and discovery markets

• New clients that need real‐time or near real‐time translation

Revenue RevenueDepending on project or product model, revenues will vary See previous slide

Preparing source dataRuntime glossary preparationrevenues will vary. See previous slide.

Additional revenues from client getting more ROI and willing to invest in new languages.

Runtime glossary preparationNon‐translatable terms definitionPost editingRecurring revenues from words translated


Expand ExistingRelationships

Expand ExistingRelationships

Added Functionality

Added FunctionalityRelationshipsRelationships FunctionalityFunctionality

• Opportunities to translate additional material for markets that may not have

• Expand service offerings with new features such as multilingual customer y

been cost viable with a human only approach

• Reuse custom MT for multiple purposes

gsupport

• Integrate machine translation into existing client technologies, products p p p

• Enable clients to better compete in markets that were only partially addressed due to cost and time

and services

Revenue Revenue

addressed due to cost and time

Preparing source dataTerminology definition

Same as for Expanding Existing RelationsTerminology definitionNon‐Translatable termsUnknown and high‐frequency phrase resolutionPost editing

Additionally able to charge various service fees relating to the new services offered. For example, translating common Q&A for


Recurring revenues from words translatedOne time revenues from resale of customization

customer support and a commission on integrated multilingual support products.

• Chat• Discussions• Translation ofTranslation of other content


Whether human or machine, where there is translation, there will be errorsWhether human or machine ‐

, ,where there is translation, there will be errors

Machine translation k l herrors make me laugh.

BUTHuman translation


errors make me cry!

Engine OutputSystran La TA no es no majican en tekt de la salsa del baady jGoogle MT no hay majican en salsa de tekt baadAsia Online TA ain't no tekt baad majican en salsa

Quality Input Delivers Quality OutputInput: MT cannot perform magic on bad source textInput: MT cannot perform magic on bad source text.Output: TA no puede hacer magia en el texto de fuente mala.

GIGOGIGOGarbage In


Garbage Out

Bad source text will result in bad translations.GIGO G b I G b O

E amples of Common Errors

GIGO – Garbage In, Garbage Out

• Bad end of sentence formatting (merged or missing periods)

Examples of Common Errors

...jumped over the lazy dog.Mary had a little lamb...

...jumped over the lazy dog Mary had a little lamb...

• Bad spelling and grammar (often from OCR or typing without spell check)

The quiak brown faux jumped over the lazy fog.

• Sentences broken by line breaks

Twas the the night christmas, when all threw the house

The quick brown


y e qu c b ofox jumped over thelazy dog.

If you wanted to evaluate a sports car, which of the following vehicles would you test drive?

Ag y

B C

Traditional Consumer Mainstream High Performance

Systran, ProMT Microsoft, Google Asia Online

Gi tGi t PublicationPublicationGistDesigned to give a general understanding of a document. Some

basic customization is available by adding dictionaries and l b t th i l t l f t l

GistDesigned to give a general understanding of a document. Some

basic customization is available by adding dictionaries and l b t th i l t l f t l

PublicationDesigned to deliver translated output that requires the least

amounts of edits in order to publish

PublicationDesigned to deliver translated output that requires the least

amounts of edits in order to publishsamples, but there is no real control of style or grammar.samples, but there is no real control of style or grammar. amounts of edits in order to publish.amounts of edits in order to publish.

Machine translation systems cannot magically understand who your customer is, what vocabulary you prefer or what writing style you desire


, y y p f g y y– all are required in order to publish.To achieve this goal, a MT system must be customized to match.

ESTranslated text can be stylized based on the style of the Monolingual data.

News paper ENpaper article

Bilingual Data Monolingual Data

Text written in the style of

business Millions of Sentence Pairs Business News

The EconomistNew York TimesForbes

news

ENChildren’s BooksHarry PotterRupert the BearFamous Five

Text written in the style of children’s Possible Vocabulary Famous Five

Spanish Original Before Translation:

Se necesitó una gran maniobra política muy prudente a fin de facilitar una cita de los dos enemigos históricos

booksy

Writing Style & Grammar

Before Translation: cita de los dos enemigos históricos.

Business News After Translation:

Significant amounts of cautious political maneuvering were required in order to facilitate a rendezvous between the two bitter historical opponents.


Children’s Books After Translation:

A lot of care was taken to not upset others when organizing the meeting between the two long time enemies.

Pre‐Processing RulesPre‐Processing Rules

Language Studio™ Hybrid Rules and SMT Engine ModelStatistical Machine TranslationStatistical Machine Translation Post‐Processing RulesPost‐Processing RulesPre Processing RulesPre Processing Rules

• Open Process• Fully CustomizableC l t C t l



Statistical Machine TranslationStatistical Machine Translation Post Processing RulesPost Processing Rules

• Complete Control• No Limits



Rules prepare the source text for better translation

Rules fine tune the SMT output and offer

Competitors “Hybrid” Rules and Corrective Statistical Engine Model

and guide the SMT processp

additional flexibility

Competitors Hybrid Rules and Corrective Statistical Engine ModelGet it right the first time!!• Black Box • Black Box

Statistical Correction of Rules ErrorsStatistical Correction of Rules ErrorsTranslation RulesTranslation Rulesf

Why treat the symptoms when

• Black Box• Where “Magic” Happens• Limited Control

• Black Box• Where “Magic” Happens• Limited Control

• Phrases Only• Dictionaries Only


y pyou can treat the cause?Band‐aid to fix a broken solution

Phrases Only• 40,000 Phrase Limit

Dictionaries Only• 20,000 Term Limit

Data CleaningData Preparation

Translate

Data Collections

Training

CollectionsDiagnostics and Fine Tuning

QualityAssurance

Language Pair Foundation Data


Original Translation Sources

g g

Domain Foundation Data

If you wanted to write an article on global monetary policy for Forbes, which specialist would your hire?

A B C

John Smith, Financial Analyst

Bruno Mann, Ex‐Pro Footballer

Kerry Adams, Mechanical Engineer

You would choose the specialist with skills in the domainIf you wanted to translate the same article into Chinese,

Mother tongue

which translator would you hire?A B C

Domain experience

John Smith, American, Financial Analyst, Learning Chinese

Brenda Jones, British, Studying Chinese History

Faye Wong, Chinese,Graduated Business & Finance at Harvard


High Quality Translation – Human and Machine Both Require Specialization

Financial Analyst, Learning ChineseStudying Chinese History Graduated Business & Finance at Harvard

Clean and Consistent DataA statistical engine learns from data in the training corpus. Language Studio Pro™ contains many tools to help ensure that the data is scrubbed clean prior to training.

Controlled DataFewer translation options for the same source segment, and “clean” translations lead to better foundation patternsclean translations lead to better foundation patterns.

Common DataHi h d t l i th bj t i f t ti ti lHigher data volume, in the same subject area, reinforces statistical relationships. Slight variations of the same information add robustness to the systems.

Current DataEnsure that the most current TM is used in the training data.


Outdated high frequency TM can have an undue negative impact on the translation output and should be normalized to current style

• Data– Gathered from as many sources as

possible

Dirty Data SMT ModelDirty Data SMT Modelpossible.

– Domain of knowledge does not matter.– Data quality is not important. – Data quantity is important.q y p

• Theory – Good data will be more statistically

relevant.relevant.

• DataGathered from a small number of

Clean Data SMT ModelClean Data SMT Model – Gathered from a small number of trusted quality sources.

– Domain of knowledge must match target

– Data quality is very important.– Data quantity is less important.

• Theory


y– Bad or undesirable patterns cannot be

learned if they don’t exist in the data.

• Bad translations• Out of domain text

U b l d / Bi d• Unbalanced / Biased– Too much text from other domains

• Mixed / Wrong language• Junk and noise• Junk and noise• Broken HTML• Mixed Encoding• Missing diacritics• Missing diacritics

– café vs. cafe• OCR Text• Machine translated textMachine translated text• Anything that is not high

quality and in domain

Put Simply: If a bad pattern does not exist in your training data you cannot generate such a


your training data, you cannot generate such a bad pattern as translation output.

English Source Human Translation Google Translation Google ContextI went to the bank Fui al banco Fui al banco Bank as in financeI went to the bank to Fui al banco para depositar Fui al banco a depositar Bank as in financeI went to the bank to deposit money

Fui al banco para depositardinero

Fui al banco a depositarel dinero

Bank as in finance

I went to the bank of h i

Fui en coche a la i li ió d l l

Fui a la orilla de la vueltai h

Bank as in river bankthe turn in my car inclinación de la vuelta en mi coche

I put my car into the bank of the turn

Puse mi coche en la inclinación de la vuelta.

Pongo mi coche en el banco de la vuelta

Bank as in finance

I swam to the bank of the river

Nadé en la orilla del río Nadé hasta la orilla del río

Bank as in river bank

I bankedmy money Depositémi dinero Yo depositadomi dinero Banked as in financey y p pI bankedmy car into the turn

Inclinémi coche en la vuelta

Yo depositadomi cocheen la vuelta

Banked as in finance

I bankedmy plane into Inclinémi avión en para Yo depositado en mi Banked as in financeI bankedmy plane into a steep dive

Inclinémi avión en parauna zambullida.

Yo depositado en mi avión en picada

Banked as in finance

The above examples show that Google is biased towards the banking and finance domainIssue:


There is much more multilingual banking and finance data available to learn from than there is aeronautical or water sports data available.Cause:

Mixing a focused set of Mixing a wide variety ofNot RecommendedRecommended

Mixing a focused set of bilingual domain data together

• Different sources are ok

Mixing a wide variety of bilingual domain data together

Do not mistake somewhat Providing large enough monolingual data to support grammar structures

related content as content in the same domain

• E g Anti virus is more in thegrammar structures • E.g. Anti‐virus is more in the security domain than the IT domain

Example of translated output influenced by anti‐virus

( i d i ) i d i IT d i Providing insufficient monolingual data to support grammar structures

Protect your documents

text (security domain) mixed into IT domain

grammar structures• The more variety in bilingual data, the more monolingual data ill b i d


will be required. Chroni komputer przed dokumentów(Protects your computer from documents)

• Typically about 10‐20 examples for each clean word of phrase.

• Each correction has statistical relevance and

• Large volumes of dirty data prohibits manual correction.

• Individual corrections are not statistically• Each correction has statistical relevance and impact can be clearly seen.

• Corrections usually involve adding data to fill gaps.

• Individual corrections are not statistically relevant.

• Manual corrections must compete against 1,000’s of bad examples. Impractical to create g p

• Far less correction of actual errors.• Clean data means cause of errors can be

understood and corrected.

, p penough examples manually.

• Understanding the cause of errors is difficult.• Slows training and overall processing time.

• Concordance used to create unbiased examples/phrases and ensure scope covered.

Requires more resources to process excess data.

• Only solution is to acquire more dirty data d h bl i fi d B


and hope problem is fixed. But may get worse or cause new errors.

Competitors Statistical MT

Language Studio Allows:• Automated identification of areas of weakness• Post Editing Feedback focusing directly on areas of

weakness• Get more dirty data• Human translate more data• Automated error pattern analysis and correction

• Analysis and Resolution of Unknown Words• Determination and resolution of high frequency

phrases

• Human translate more data

Competitors Rule Base MTp

• Terminology Extraction• Balancing Bilingual Phrases against Monolingual Data• Runtime glossary• Runtime spelling dictionaryRuntime spelling dictionary• Pattern handling and adjustments• Incremental Improvement Training• Automated Quality Measurement• Human Quality Measurement

• Add dictionary entries (limit 20K words)


• Human Quality Measurement • Quality Confidence Scores for each segment • Train a language model to fix broken

rules output (limit 40K phrases)

Example of Training DataMore initial data provided for training results in greater vocabulary and grammatical coverage above the

Sufficient Data Threshold

Volume

Sufficient Data Threshold and less post editing feedback required.

Data Shortfall

Post Edited Feedback andGenerated Data to Fill Gaps

Data V

T i i d f h i d f d i h

Gaps in Topic Coverage

• Training data can often have gaps in coverage and an excess of data in other areas.• Gaps in coverage reduce translation quality. • Gaps can quickly be filled via post editing the machine translated output and submitting

the data back to the system for further learningthe data back to the system for further learning.• Many gaps can be filled with monolingual data only.• Further gaps can be identified and resolved by analyzing the text that is to be translated

for high frequency terms and unknown words


g q y• In some cases incorrect data may be statistically more relevant. Post editing will raise the

relevance of the correct grammar.

Before Machine TranslationBefore Machine TranslationSource text is processed and modifiedPre‐Translation Corrections (PTC)‐ A list of terms that adjust the source text fixing common issues and

Source text is processed and modified.

text fixing common issues and making it more suitable for translation.Runtime Glossary (RTG)A list of bilingual terms that are used to

After Machine TranslationAfter Machine Translation‐ A list of bilingual terms that are used to ensure terminology is translated a specific way.Non Translatable Terms (NTT)

Target text is processed and modified.

Post Translation Adjustment (PTA)A list of terms in the target language thatNon‐Translatable Terms (NTT)

‐ A list of monolingual terms that are used to ensure key terms are not translated

‐ A list of terms in the target language that modify the translated output. This is very useful for normalization of target terms.

translated.

Each of the above runtime customizations can be applied in 2 forms:D f l A li d ll j b


Default: Applied to all jobs.Job Specific: A different set of customizations can be applied for different clients.

Initial System put into production p

Changes are collected and added to initial corpus to drive continuous retraining

Trained Internal Experts begin initial clean up and correction process

All users allowed to suggest changes which goes through vetting process

Expert Users also allowed to make changesg p g

Publication Quality Target

Post Editing EffortQua

lity

Raw MT Quality

PostPost‐‐editing effort and cost can be managed by editing effort and cost can be managed by improving the quality and performance of the improving the quality and performance of the MT engine via corrective linguistic feedback MT engine via corrective linguistic feedback

Copyright © 2011, Asia Online Pte LtdEngine Learning Iteration

1 2 543 6

CorrectMi l i

Human Feedback

Targeted Corrections

Key

MistranslationSyntax/GrammarTerminology

S lli d

Targeted Corrections of Bad Learning

CorrectgySpellingPunctuation

Spelling and Terminology

Correct

Initial SystemCorrect

Correct

Correct


Human Feedback can raise the raw output to previously unseen quality levels

Human OnlyHuman Only MT + HumanMT + HumanMT OnlyMT Only

• Terminology Definition• Non‐Translatable Terms• Historical Translations

l id


S l G id **


St l G id **• Style Guide• Quality Requirements• Translate

Edit

• Style Guide**• Quality Requirements• Customize MT• Translate

• Style Guide**• Quality Requirements• Customize MT• Translate• Edit

• Proof• Project Management

• Translate• Edit• Proof• Project Management

• Translate• Edit• Proof• Project Management • Project Management

When preparing for a high quality human translation project, many core steps are performed in order to ensure that the writing style

• Project Management

Almost identical information and data is required in order to

core steps are performed in order to ensure that the writing style and vocabulary are designed for a customers target audience.


Almost identical information and data is required in order to customize a high quality machine translation system

Sajan's experience with MT customizationIñ ki H á d LIñaki Hernández‐Lasa


B i S i f B ildiBusiness Strategies for Building Strategic Advantage and Revenue

from Machine Translation

Dion WigginsChief Executive OfficerChief xecutive [email protected]


bibusiness sis trategies for b ildib uilding …...title microsoft powerpoint - asia online - lrc-...

Documents