Improving AMBER, an MT Evaluation Metric
Boxing Chen, Roland Kühn, George Foster · NPARC · 2012
A recent paper described a new machine translation evaluation metric, AMBER. This paper describes two changes to AMBER. The first one is incorporation of a new ordering penalty; the second one is the use of the downhill simplex algorithm to tune the weights for the components of AMBER. We tested the impact of the two changes, using data from the WMT metrics task. Each of the changes by itself improved the performance of AMBER, and the two together yielded even greater improvement, which in some cases was more than additive. The new version of AMBER clearly outperforms BLEU in terms of correlation with human judgment. 1