Paper: Using A Mixture Of N-Best Lists From Multiple MT Systems In Rank-Sum-Based Confidence Measure For MT Outputs

ACL ID C04-1047
Title Using A Mixture Of N-Best Lists From Multiple MT Systems In Rank-Sum-Based Confidence Measure For MT Outputs
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004
Authors

This paper addressees the problem of eliminat- ing unsatisfactory outputs from machine trans- lation (MT) systems. The authors intend to eliminate unsatisfactory MT outputs by using confidence measures. Confidence measures for MT outputs include the rank-sum-based confi- dence measure (RSCM) for statistical machine translation (SMT) systems. RSCM can be ap- plied to non-SMT systems but does not always work well on them. This paper proposes an alternative RSCM that adopts a mixture of the N-best lists from multiple MT systems instead of a single-system’s N-best list in the exist- ing RSCM. In most cases, the proposed RSCM proved to work better than the existing RSCM on two non-SMT systems and to work as well as the existing RSCM on an SMT system.