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Multi-Engine Machine Translation

Multi-Engine Machine Translation. Stephan Vogel, Alon Lavie, Jaime Carbonell Bob Frederking, Ralf Brown Language Technologies Institute Carnegie Mellon University 5000 Forbes Av. Pittsburgh, PA 15213 Email: {vogel+, alavie, jgc}@cs.cmu.edu. MEMT Scenario.

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Multi-Engine Machine Translation

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  1. Multi-Engine Machine Translation Stephan Vogel, Alon Lavie, Jaime Carbonell Bob Frederking, Ralf Brown Language Technologies Institute Carnegie Mellon University 5000 Forbes Av. Pittsburgh, PA 15213 Email: {vogel+, alavie, jgc}@cs.cmu.edu

  2. MEMT Scenario • Text from different knowledge sources • Translated by different translation systems (MT) • Selection of best translation; Creation of better translation • Distributed to different NLP systems or analysts NE Trl SUM. NE Tagger MT 2 TDT OCR MEMT MT 3 TCluster XLIR MT 4 MT 5 ITIC MT Integration Meeting

  3. Objective and Approach • Provide improved translation quality by combining output from different translation systems • MEMT granularity • Sentence level: select 1 out of n translations based on translation model (scoring scheme) • Sub-sentential level: find better translation by combining translation fragments (build translation lattice; single-best search) • Baseline: rescore all translations with same statistical translation model, use SMT decoder • Extensions: • Take scores assigned by different MT engines into account • Classification methods for scoring translation fragments • Confidence measures • Optimization according to MT metric ITIC MT Integration Meeting

  4. The more information the better Input/Output • Input: Translations from multiple systems • Translation lattice: partial translations annotated with scores • N-best list annotated with scores • First-best or only translation with/without scores • Output: Single best translation (N-best list possible) • Format: Text format as simple as possible • String • Lattice BEGIN_LATTICE0 1 “source word” “target words” total_score score_1 score_2 …0 3 “source words” “target words” total_score score_1 score_2 … … END_LATTICE • Segmentation issues: same input for all MT systems ITIC MT Integration Meeting

  5. Progress Tracking • Compare performance of MEMT output to performance of individual MT systems • Automatic (objective) MT evaluation metrics • General: Bleu and modified Bleu metrics (METEOR, NIST MTeval) • Specific: NE evaluation • Oracle: how well can an “ideal” MEMT system hope to perform? Find best-scoring translation in the lattice (according to the automatic metrics) • Human evaluation • To ascertain that automatic evaluation metrics are measuring the right thing ITIC MT Integration Meeting

  6. Hindi-to-English translation 3 MTs: Transfer-based, example-based, statistical Recent Results: Baseline System ITIC MT Integration Meeting

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