System Combination Using Discriminative Cross-Adaptation

Cross-adaptation (CA) based methods of machine translation (MT) system combi-nation work by adapting the decoding step of a baseline system using information from alternate systems. Generally, the re-quired information is very deep, such as a full decoding forest. In this paper, we de-scribe a method of cross-adaptation based system combination which only requires the final output from each alternate sys-tem. This is achieved by adding a dis-criminatively weighted n-gram confidence feature to our decoder. In order to opti-mize the confidence weight of each sys-tem, we present a novel procedure called non-linear Expected-BLEU optimization that can be used to optimize arbitrary non-linear parameters for any decoding fea-ture. We also describe a method for explic-itly creating an adapted system that is dis-similar from each particular input system, which we have found to be useful in com-bination. Although our new method does not outperform a state-of-the-art confusion network (CN) based combination system on its own, we obtain statistically signif-icant gains of 0.21-0.45 BLEU when the CA output is used as an additional system in CN combination. ∗This work was supported by DARPA/I2O Contract No. HR0011-06-C-0022 under the GALE program (Approved for