1. Machine Translation The Translator s Choice Heidi Düchting Sylke Krämer Johann Roturier

2. Outline Background Challenges Solutions Benefits Next steps Conclusions

3. Commercial Imperatives Effective –Time-critical documents in volume Efficient –Translation process automation –Combining translation technologies workflow TM, MT, and PE tools Control –Loose writing guidelines vs. Controlled Language rules Improved machine translatability

4. Commercial Systems Combine technologies TM with previously machine-translated and post-edited segments for look-up TM systems with MT component Rule based and Example based Pre-translate phase Towards improved post-editing efficiency? Not available in all systems MT systems with TM component 100 % match look-up

5. Challenges Setting a threshold for TM matches –100% matches only suitable when the objective is to provide MT output for gisting (no post-editing) suitable when the MT system is fully customized and CL environment is in place (no post-editing?) Quick PE New sentences in which only one character changes are sent to the MT engine –W32.Beagle.AB is a mass-mailing worm that neither propagates via network shares nor deletes files –W32.Beagle.AC is a mass-mailing worm that neither propagates via network shares nor deletes files

6. Solutions (1) Two-tier process Leverage Trados TM repository Use MT system to translate unknown segments (Systran Premium 5.0) Use MT output as TM input Determine the export threshold Existing TM segments vs. new controlled segments –Uncontrolled: Symantec announced a patch was available –CL: Symantec announced that a patch was available

7. Solutions (2) TMX format obvious choice as the exchange format XLIFF not supported by all MT systems source and target segments Then the worm searches all local and network drives for.gif,.bmp, and.wav files. Then the worm searches all local and network drives for.gif,.bmp, and.wav files.

8. Processing TMX Technical issues TMX's various implementations can create discrepancies during the exchange process Identical source and target segment XML parser and TMX header Pre and post processing with a single macro Modules to remove and restitute sections Environment: VBA

9. Pre-translation Workflow Step 6: Import segments into TM Step 5: Post- processing module Step 4: Call to MT system Step 3: Pre- processing module Step 2: Export unmatched segments Step 1: Analyze new document

10. Effective pre-translation Efficiency and robustness Refinable Opportunity for modifications Target segments CL environment predictability Frequent errors Ideal scenario Address problems that could not be fixed with CL rules

11. Towards Automated Post-Editing Surface post-editing No linguistic analysis: no second MT Text processing Frequent errors due to default MT settings Remove drudgery from post-editing Lexical Capitalization (folgende vs. Folgende) Incorrect spelling (neuzustarten vs. neu zu starten) Missing contractions (à le vs. au) Extra words (fichier de.bmp vs. fichier.bmp)

12. Towards Automated Post-Editing Syntactic Word order: Klicken auf Sie vs. Klicken Sie auf Wrong structures (transfer or generation issue): neither…nor (ni ne..ni ne) Textual Formatting: trailing spaces after symbols (backslashes) Punctuation inconsistent with style guide: inverted commas for German

13. Towards Automated Post-Editing Suitability of the environment Regular expressions support RE are a way to describe text through pattern matching (Stubblebine 2003: 1) Grouping and Capturing: 1.Match: ([Kk]licken) (auf) (Sie) 2.Replace: \1 \3 \2

14. Content workflow

16. Next steps New environment –GMS integration Centralized interface with content Transport layer MT as plug-in –XLIFF format To machine translate unmatched segments –PE replacements Fine-tune contextual replacements

17. Conclusions Combining MT & TM is efficient leverage post-editing is not repeated increased throughput Environment for avoiding errors facilitated when CL rules are introduced Scope of errors is reduced New opportunities for translators Fine-tuning MT user dictionaries Refine automated PE tasks

18. Thank You johann_roturier@symantec.com