Unsupervised False Friend Disambiguation Using Contextual Word Clusters and Parallel Word Alignments

Lexical false friends (FF) are the phenomena where words that look the same, do not have the same meaning or lexical usage. FF impose several challenges to statistical machine translation. We present a methodology which exploits word context modeling as well as information provided by word alignments for identifying false friends and choosing the right sense for them in the context. We show that our approach enhances SMT lexical choice for false friends across language variants. We demonstrate that our approach reduces word error rate (WER) and position independent error rate (PER) for Egyptian-English SMT by 0.6% and 0.1% compared to the baseline.