Paper: Improving Alignment of System Combination by Using Multi-objective Optimization

ACL ID D13-1051
Title Improving Alignment of System Combination by Using Multi-objective Optimization
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

This paper proposes a multi-objective opti- mization framework which supports heteroge- neous information sources to improve align- ment in machine translation system combi- nation techniques. In this area, most of techniques usually utilize confusion networks (CN) as their central data structure to com- pact an exponential number of an potential hy- potheses, and because better hypothesis align- ment may benefit constructing better quality confusion networks, it is natural to add more useful information to improve alignment re- sults. However, these information may be het- erogeneous, so the widely-used Viterbi algo- rithm for searching the best alignment may not apply here. In the multi-objective opti- mization framework, each information source is viewed as an independent objective, and...