Ah OK, I misunderstood, I thought you were talking about more advanced pruning techniques compared to the significance method from Johnson et al. while you only referred to the 30-best variant. Cheers, Marcin
On 19.06.2015 19:35, Rico Sennrich wrote: > Marcin Junczys-Dowmunt <junczys@...> writes: > >> Hi Rico, >> since you are at it, some pointers to the more advanced pruning >> techniques that do perform better, please :) >> >> On 19.06.2015 19:25, Rico Sennrich wrote: >>> [sorry for the garbled message before] >>> >>> you are right. The idea is pretty obvious. It roughly corresponds to >>> 'Histogram pruning' in this paper: >>> >>> Zens, R., Stanton, D., Xu, P. (2012). A Systematic Comparison of Phrase >>> Table Pruning Technique. In Proceedings of the 2012 Joint Conference on >>> Empirical Methods in Natural Language Processing and Computational >>> Natural Language Learning (EMNLP-CoNLL), pp. 972-983. >>> >>> The idea has been described in the literature before that (for instance, >>> Johnson et al. (2007) only use the top 30 phrase pairs per source >>> phrase), and may have been used in practice for even longer. If you read >>> the paper above, you will find that histogram pruning does not improve >>> translation quality on a state-of-the-art SMT system, and performs >>> poorly compared to more advanced pruning techniques. > > the Zens et al. (2012) paper has a nice overview. significance > pruning and relative entropy pruning are both effective - you are not > guaranteed improvements over the unpruned system (although Johnson (2007) > does report improvements), but both allow you to reduce the size of your > models substantially with little loss in quality. > > _______________________________________________ > Moses-support mailing list > Moses-support@mit.edu > http://mailman.mit.edu/mailman/listinfo/moses-support _______________________________________________ Moses-support mailing list Moses-support@mit.edu http://mailman.mit.edu/mailman/listinfo/moses-support
