We present a novel paradigm for statistical machine translation (SMT), based on a joint modeling of word alignment and the topical aspects underlying bilingual document-pairs, via...
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
In this paper, we adapt a statistical learning approach, inspired by automated topic segmentation techniques in speech-recognized documents to the challenging protein segmentation ...
Betty Yee Man Cheng, Jaime G. Carbonell, Judith Kl...
This paper1 presents an empirical approach to mining parallel corpora. Conventional approaches use a readily available collection of comparable, nonparallel corpora to extract par...
We propose a rule-based approach for transforming B abstract machines into UML diagrams. We believe that important insight into the structure underlying a B model can be gained by...