Flexible finite-state lexical selection for rule-based machine translation

Abstract

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.