We integrate semantic information at two stages of the translation process of a state-ofthe-art SMT system. A Word Sense Disambiguation (WSD) classifier produces a probability distribution over the translation candidates of source words which is exploited in two ways. First, the probabilities serve to rerank a list of n-best translations produced by the system. Second, the WSD predictions are used to build a supplementary language model for each sentence, aimed to favor translations that seem more adequate in this specific sentential context. Both approaches lead to significant improvements in translation performance, highlighting the usefulness of source side disambiguation for SMT.
Likewise, REF ) use a WSD classifier to generate a probability distribution of phrase pairs and to build a local language model.
WSD for n-best reranking and local language modeling in SMT
Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation
Computer Science