Applying Pairwise Ranked Optimisation to Improve the Interpolation of Translation Models
Barry Haddow · 2013
In Statistical Machine Translation we often have to combine different sources of parallel training data to build a good system. One way of doing this is to build separate translation models from each data set and linearly inter-polate them, and to date the main method for optimising the interpolation weights is to min-imise the model perplexity on a heldout set. In this work, rather than optimising for this indi-rect measure, we directly optimise for BLEU on the tuning set and show improvements in average performance over two data sets and 8 language pairs. 1