Presented at the 6th Language Technology Conference, Cordoba, Argentina, April 2009

1. Bob Donaldson, VP Strategy Good Applications of Bad Machine Translation

2. Promise of MT or eMpTy Promises? Anything worth doing is worth doing poorly.

5. The Famous Triangle MT + Post-Edit Improvements Currently translated by humans } MT Extensions – Reduced Quality

6. The Famous Triangle Eventual MT success in narrow domain MT + Post-Edit Improvements Currently translated by humans

13. MT + Search + HT = Cost-Effective Solution SMT TM SMT HT Review TM SMT Post-Edit TM SMT Quality of translation SMT Entire Corpus Translated for Human Reader Total Cost Proportionate to Quality & Volume Assumes Fully Trained SMT HT* * Minimal Training SMT HT* Rough MT of Entire Corpus Translated for Index/Search Lower Overall Cost plus Highest Quality * On Demand

14. Data Aggregation Perspective Rough MT … may not be “human ready” “ On-demand” human translation Analytics to support ‘triage'

16. Eg: Unified Legal Analysis Environment English Document Set Translated Document Set IPX Document Profiling Process Document-Specific Concept Profiles Translation Process: ~1% HT ~99% MT IPX NLP Process Keyword Translation Basic Priority Scoring Process IPX NLP Process Unified Document Set Unified Correlation Matrices French Document Set Paralegal Analysis