We propose a novel HMM-based framework to accurately transliterate unseen named entities. The framework leverages features in letteralignment and letter n-gram pairs learned from ...
Bing Zhao, Nguyen Bach, Ian R. Lane, Stephan Vogel
In this paper we use statistical machine translation and morphology information from two different morphological analyzers to try to improve translation quality by linguistically ...
We propose a language-independent approach for improving statistical machine translation for morphologically rich languages using a hybrid morpheme-word representation where the b...
We improve the quality of statistical machine translation (SMT) by applying models that predict word forms from their stems using extensive morphological and syntactic information...
In this paper we report our recent development of an end-to-end integrative design methodology for speech translation. Specifically, a novel decision function is proposed based o...