Learning synchronous context-free grammars with multiple specialised non-terminals for hierarchical phrase-based translation{}^{*}
Table 4: BLEU scores obtained by the k-medoids clustering method for different sizes of the subset of non-terminals over which the clustering is performed and for different numbers of clusters (i.e. final non-terminals). The performance of the baseline HSMT system is 0.5818.
['# clustered non-terminals', '# final non-terminals  [ITALIC] k 2', '# final non-terminals  [ITALIC] k 3', '# final non-terminals  [ITALIC] k 4']
most frequent non-terminals (# clustered non-terminals) to obtained a pre-fixed number of clusters, that is, of non-terminals in the inferred grammar ; the remaining non-terminals are then added to the nearest cluster after the algorithm finishes. The table reports results for 2, 3 and 4 clusters; although we tried with more clusters these are the numbers of clusters for which we got the best results. The difference in performance with the baseline is statistically significant for all the figures reported in the table according to paired bootstrap resampling [koehn04:_statis] with p=0.05.