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Enhancing Machine Translation Quality Through Comprehensive Text Analysis and Provenance Tracking

This guide explores the methodologies to enhance machine translation (MT) quality by leveraging text analysis, terminology identification, and confidence metrics. It covers key processes such as post-editing, workflow analysis, and content I18N (internationalization), with a focus on tools like OKAPI and M4LOC for efficient MT. Provenance tracking ensures the integrity and accuracy of translated content, allowing for better quality checks and leverage of existing terminology. Discover the integration of HTML5 and XLIFF for efficient content management and publish workflows.

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Enhancing Machine Translation Quality Through Comprehensive Text Analysis and Provenance Tracking

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  1. UL/TCD round trip XLIFF Identify Terms Machine Translate Text Analysis Terminology Text Analysis Post Edit Domain Translate Segment ITS Quality Check Extract MT Confidence HTML5 CMIS Localisation Quality Issue/Rating HTML5 CMIS PublishContent Content I18N Provenance Workflow Analysis Curate Corpora RDF: PROV NIF

  2. ENLASO round trip OKAPI Machine Translate Quality check TM Leverage Terminology Text Analysis Create TransKit Domain Translate Segment ITS XLIFF Translation Environment Extract MT Confidence Localisation Quality Issue/Rating HTML5 or XML Quality check Content I18N Provenance TranskitPostprocessing OKAPI Publish Content HTML5 or XML

  3. UL/TCD round trip XLIFF Identify Terms MT Matrex Text Analysis Terminology Text Analysis MT Bing Domain Translate Segment ITS OKAPI M4LOC MT Confidence Extract Provenance HTML5 Postedit Content I18N

  4. Olaf’s version XLIFF IdentifyTerms MachineTranslate Text Analysis Text Analysis Terminology Post Edit Domain Translate Segment ITS QualityCheck Extract Confidence Quality MT Rating HTML5 CMIS HTML5 CMIS Localisation Publish Content Issue/ Content I18N Provenance WorkflowAnalysis CurateCorpora RDF: PROVNIF

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