In this paper we describe a module (rule formalism, rule compiler and rule processor) designed to provide flexible support for lexical selection in rule-based machine translation. The motivation and implementation for the system is outlined and an efficient algorithm to compute the best coverage of lexical-selection rules over an ambiguous input sentence is described. We provide a demonstration of the module by learning rules for it on a typical training corpus and evaluating against other possible lexical-selection strategies. The inclusion of the module, along with rules learnt from the parallel corpus provides a small, but consistent and statistically-significant improvement over either using the highest-scoring translation according to a target-language model or using the most frequent aligned translation in the parallel corpus which is also found in the system’s bilingual dictionaries.

Support of the Spanish Ministry of Science and Innovation through project TIN2009-14009-C02-01, and the Universitat d’Alacant through project GRE11-20.