Speech and Language Technologies in the Next Generation Localisation CSET

Prof. Andy Way, School of Computing, DCU

Overview of PresentationSpeech & Language Technologies in the NGL CSET

Overview of PresentationSpeech & Language Technologies in the NGL CSETFacilitating Optimal Multilingual NGL Applications

Overview of PresentationSpeech & Language Technologies in the NGL CSETFacilitating Optimal Multilingual NGL ApplicationsKey Research Challenges

Overview of PresentationSpeech & Language Technologies in the NGL CSETFacilitating Optimal Multilingual NGL ApplicationsKey Research ChallengesNovel Research Tracks

Overview of PresentationSpeech & Language Technologies in the NGL CSETFacilitating Optimal Multilingual NGL ApplicationsKey Research ChallengesNovel Research TracksTypical LSPs Translation Process

Overview of PresentationSpeech & Language Technologies in the NGL CSETFacilitating Optimal Multilingual NGL ApplicationsKey Research ChallengesNovel Research TracksTypical LSPs Translation ProcessKey Integration Challenges

Overview of PresentationSpeech & Language Technologies in the NGL CSETFacilitating Optimal Multilingual NGL ApplicationsKey Research ChallengesNovel Research TracksTypical LSPs Translation ProcessKey Integration ChallengesConcluding Remarks

ILT - Integrated Language TechnologiesProf. Andy WayILT Area Coordinator

ILT: Facilitating Optimal Multilingual NGL Applications

Machine Translation

Text InputText OutputText Processinge.g. bulk localisation

ILT: Facilitating Optimal Multilingual NGL Applications

Speech Technologies

Machine Translation

Text InputText OutputSpeech OutputSpeech InputText Processinge.g. bulk localisatione.g. personalisation

Machine Translation: SignificanceFor our industrial partners, volume of material needing translation increasing, while budgets remain the sameIn the EU, now 23 official languages (506 language pairs), and expanding In the US, huge investment in translation between Arabic, Chinese and UrduEnglish

Machine Translation: SignificanceFor our industrial partners, volume of material needing translation increasing, while budgets remain the sameIn the EU, now 23 official languages (506 language pairs), and expanding In the US, huge investment in translation between Arabic, Chinese and UrduEnglish

Automation the only option (especially for PL)

Enhanced Translation Quality

MT: Key Research Challenges

Enhanced Translation QualityFaster Translation TimesScalabilityOther Modalities (Speech, SMS etc.)

The State-of-the-Art Source:Reference: The two sides highlighted the role of the World Trade Organization (WTO)Baseline: The two sides on the role of the WTO

Improving the State-of-the-Art

Our MT systems have knowledge of syntaxParts of speech (nouns, verbs etc.)Roles in sentences (subject, object etc.)

better translation qualitySource:Reference: The two sides highlighted the role of the World Trade Organization (WTO)Baseline: The two sides on the role of the WTOOur System: The two sides reaffirmed the role of the WTO

The State-of-the-Art Source:Reference: Mahmoud Abbas: The wall and settlements will not bring Israel securityBaseline: Mahmoud Abbas, the wall and settlements will provide security to IsraelOur System: Mahmoud Abbas, the wall and settlements will not provide security for Israel

Improving the State-of-the-Art

better translation quality (especially where end-users are concerned)DCU ArabicEnglish system ranked first at international MT evaluation in Oct. 2007Source:Reference: Mahmoud Abbas: The wall and settlements will not bring Israel securityBaseline: Mahmoud Abbas, the wall and settlements will provide security to IsraelOur System: Mahmoud Abbas, the wall and settlements will not provide security for Israel

MT Novel Research: Handling Different Types of Text

Translating patent applications, or doctors prescriptions, or visa applications: different tasks, as the content is different So is the form

MT Novel Research: Handling Different Types of Text

Translating patent applications, or doctors prescriptions, or visa applications: different tasks, as the content is different So is the form Build different MT systems for each different task, using our industrial partners documentation

Text Processing: Significance and Challenges

If texts are automatically annotated with:

syntactic information (e.g. subject, object), todays MT systems can learn syntax required for improved output quality and improved processing of multilingual queries (DCM)

Text Processing: Significance and Challenges

If texts are automatically annotated with:

syntactic information (e.g. subject, object), todays MT systems can learn syntax required for improved output quality and improved processing of multilingual queries (DCM)

text-type and genre information, this helps our MT systems disambiguate text and improve translation quality

Text Processing: Significance and Challenges

If texts are automatically annotated with:

syntactic information (e.g. subject, object), todays MT systems can learn syntax required for improved output quality and improved processing of multilingual queries (DCM)

text-type and genre information, this helps our MT systems disambiguate text and improve translation quality

localisation information (e.g. Andy Way), then the workflows of our industrial partners (currently done manually) can be significantly improved (cf. LOC)

Speech Technology: SignificanceSpeech interfaces for eyes-busy, hands-busy scenairos

Speech recognition and synthesis systems which can deal withpotentially an unlimited vocabularymultiple (and non-native) speakersmultiple languages

and can be tightly integrated with MT

localisation & personalisation volume & scalability access

themoreitsnowsthemoreitgoesSpeech Technology: Challenges

themoreitsnowsthemoreitgoesdemoreisnowsdemoregoesSpeech Technology: Challenges

themoreitsnowsthemoreitgoeslinguistic competence of native speakerrules and vocabulary of systemperformance of (native) speakerSpeech Technology: Challengesdemoreisnowsdemoregoes

themoreitsnowsthemoreitgoeslinguistic competence of native speakerperformance of (native) speakerSpeech Technology: Innovationswhich integrates explicit linguistic knowledgeRobust & Novel Speech Recognition Enginedemoreisnowsdemoregoes

themoreitsnowsthemoreitgoesdetverkarhavaritenstorstormhurmnlinguistic competence of native speakerJemehreschneitdestomehresgehtInnovations: Speech Recognition & MTRobust & Novel Speech Recognition EngineTight coupling with MT Engineswhich integrates explicit linguistic knowledge

themoreitsnowsthemoreitgoesdetverkarhavaritenstorstormhurmnJemehreschneitdestomehresgehtInnovations: MT & Speech SynthesisRobust & Novel Speech Synthesis Enginewhich integrates explicit linguistic knowledgeTight coupling with MT Engines

Typical LSPs Translation Process Requirement: minimal disruption of this process& Machine TranslationTM match score < 50 %: expensive

50 % < TM match score < 70 %: medium

TM match score > 70 %: cheap

Key Integration ChallengesUse MT to automatically upgrade some TM matches to a cheaper cost class, cf. Dynamic Translation Memory [Bicici and Dymetman, 2008]

Key Integration ChallengesUse MT to automatically upgrade some TM matches to a cheaper cost class, cf. Dynamic Translation Memory [Bicici and Dymetman, 2008]Linking MT automatic evaluation metrics with post-editing cost

Key Integration ChallengesUse MT to automatically upgrade some TM matches to a cheaper cost class, cf. Dynamic Translation Memory [Bicici and Dymetman, 2008]Linking MT automatic evaluation metrics with post-editing costEnsuring that MT omissions are highlighted

Key Integration ChallengesUse MT to automatically upgrade some TM matches to a cheaper cost class, cf. Dynamic Translation Memory [Bicici and Dymetman, 2008]Linking MT automatic evaluation metrics with post-editing costEnsuring that MT omissions are highlightedEnforcing customer terminology

Key Integration ChallengesUse MT to automatically upgrade some TM matches to a cheaper cost class, cf. Dynamic Translation Memory [Bicici and Dymetman, 2008]Linking MT automatic evaluation metrics with post-editing costEnsuring that MT omissions are highlightedEnforcing customer terminologyDeal with markup, tags

Key Integration ChallengesUse MT to automatically upgrade some TM matches to a cheaper cost class, cf. Dynamic Translation Memory [Bicici and Dymetman, 2008]Linking MT automatic evaluation metrics with post-editing costEnsuring that MT omissions are highlightedEnforcing customer terminologyDeal with markup, tags Produce true-cased translations

Key Integration ChallengesUse MT to automatically upgrade some TM matches to a cheaper cost class, cf. Dynamic Translation Memory [Bicici and Dymetman, 2008]Linking MT automatic evaluation metrics with post-editing costEnsuring that MT omissions are highlightedEnforcing customer terminologyDeal with markup, tags Produce true-cased translations

Integrate into pre-existing workflows!

Concluding RemarksFor ILT, ramp up almost complete, c. over 30 new researchers in addition to pre-existing PIs, postdoctoral researchers and PhD students

Concluding RemarksFor ILT, ramp up almost complete, c. over 30 new researchers in addition to pre-existing PIs, postdoctoral researchers and PhD studentsLarge interest from industrial partners, both large and small

Concluding RemarksFor ILT, ramp up almost complete, c. over 30 new researchers in addition to pre-existing PIs, postdoctoral researchers and PhD studentsLarge interest from industrial partners, both large and small Input from LOC, DCM and SF

Concluding RemarksFor ILT, ramp up almost complete, c. over 30 new researchers in addition to pre-existing PIs, postdoctoral researchers and PhD studentsLarge interest from industrial partners, both large and small Input from LOC, DCM and SFSignificant role in CNGL demonstrators

Concluding RemarksFor ILT, ramp up almost complete, c. over 30 new researchers in addition to pre-existing PIs, postdoctoral researchers and PhD studentsLarge interest from industrial partners, both large and small Input from LOC, DCM and SFSignificant role in CNGL demonstratorsResearch tools Industrial prototypes

Concluding RemarksFor ILT, ramp up almost complete, c. over 30 new researchers in addition to pre-existing PIs, postdoctoral researchers and PhD studentsLarge interest from industrial partners, both large and small Input from LOC, DCM and SFSignificant role in CNGL demonstratorsResearch tools Industrial prototypesWell placed to succeed in going beyond TMs

Speech & Language Technologies in the NGL CSET

Thanks for listening!

Questions?

http://www.cngl.ie

away@computing.dcu.ie