Paper: Feasibility of Human-in-the-loop Minimum Error Rate Training

ACL ID D09-1006
Title Feasibility of Human-in-the-loop Minimum Error Rate Training
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

Minimum error rate training (MERT) in- volves choosing parameter values for a machine translation (MT) system that maximize performance on a tuning set as measured by an automatic evaluation met- ric, such as BLEU. The method is best when the system will eventually be eval- uated using the same metric, but in reality, most MT evaluations have a human-based component. Although performing MERT with a human-based metric seems like a daunting task, we describe a new metric, RYPT, which takes human judgments into account, but only requires human input to build a database that can be reused over and over again, hence eliminating the need for human input at tuning time. In this investigative study, we analyze the diver- sity (or lack thereof) of the candidates pro- duced during MERT, we describe ...