Machine Translation Proposal
Machine Translation Proposal Pilot Objective: Cost : PEMT 27% - - PowerPoint PPT Presentation
Machine Translation Proposal Pilot Objective: Cost : PEMT 27% - - PowerPoint PPT Presentation
Machine Translation Proposal Pilot Objective: Cost : PEMT 27% savings over human translation Efficiency : PEMT 25% faster than human translation Quality : an acceptable score under 30 according to the Harmonized the TAUS Dynamic
Pilot Objective:
- Cost: PEMT 27% savings over human translation
- Efficiency: PEMT 25% faster than human translation
- Quality: an acceptable score under 30 according to the
Harmonized the TAUS Dynamic Quality Framework (DQF) and Multidimensional Quality Metrics (MQM)
- mission
Pilot Project Processes & Problems
Step 1: File Preparation
PDF DOCX Delete the unnecessary content Delete the extra space Make the file clear; the alignment easier
Step2: Testing and Training
Round A: tmx only Round B: adding related PDFs BLEU score increased
Step3 First Round of Human Evaluation
❖ 2 post-editors ❖ A sample of 1000 words extracted from one of the system ❖ Analyzed and gave the quality score first ❖ average PE time: 53 minutesStep4: Tuning
Failed to train the system put them into training data it works:)!
Step 5: Adding dictionary
❖ 512-page IMF glossary ❖ Converted from PDF to DOCX ❖ Cleaned up formats and terms ❖ Added it into the dictionary data ❖ Trained two systemsProblem
Time consuming glossary clean-up
Problem
Failed to add the glossary into dictionary data
Problem
Lower BLEU score after adding the glossary
Step 6: The final round of human evaluation
❖ 2 post-editors ❖ A sample of 1000 words extracted from the system with the highest BLEU score(44.03) ❖ Analyzed and gave the quality score first ❖ Average PE time: 0.68 hoursProblem
Mistranslated and untranslated MT due to incomplete manual cleanup
Pilot Project Results
85%
Efficiency
71%
Cost
HT: ❖ Translation: $0.12/word ❖ Editing: $0.05/word PEMT: ❖ Post-editing: $0.05/word31.5
Quality
QA Error Score: 49 31.5 PE time for 1000 words: 53mins 40.8mins
Comparison of two rounds of human evaluation
Quality
Lessons Learned
PDF formating cleanup
When in doubt, check the system
➔ The system IS objective ➔ TMX IS better than PDF
Content relevance is key
➔“Terminology” and ”Fluency” performance are improved ➔Further data needs to be collected for assessment accuracy.