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This paper describes the research efforts of the “Hidden Speaking Mode” group participating in the 1996 summer workshop on speech recognition. The goal of this project is to model pronunciation variations that occur in conversational speech in general and, more specifically, to investigate the use of a hidden speaking mode to represent systematic variations(More)
We present a simple preordering approach for machine translation based on a featurerich logistic regression model to predict whether two children of the same node in the source-side parse tree should be swapped or not. Given the pair-wise children regression scores we conduct an efficient depth-first branch-and-bound search through the space of possible(More)
Accurately modelling pronunciation variability in conversational speech is an important component for automatic speech recognition. We describe some of the projects undertaken in this direction at WS97, the Fifth LVCSR Summer Workshop, held at Johns Hopkins University, Baltimore, in July-August, 1997. We first illustrate a use of hand-labelled phonetic(More)
We propose the use of neural networks to model source-side preordering for faster and better statistical machine translation. The neural network trains a logistic regression model to predict whether two sibling nodes of the source-side parse tree should be swapped in order to obtain a more monotonic parallel corpus, based on samples extracted from the(More)
This paper describes the research efforts of the “Hidden Speaking Mode” group participating in the 1996 summer workshop on speech recognition. The goal of this project is to model pronunciation variations that occur in conversational speech in general and, more specifically, to investigate the use of a hidden speaking mode to represent systematic variations(More)
We present a novel scheme to combine neural machine translation (NMT) with traditional statistical machine translation (SMT). Our approach borrows ideas from linearised lattice minimum Bayes-risk decoding for SMT. The NMT score is combined with the Bayes-risk of the translation according the SMT lattice. This makes our approach much more flexible than(More)
We introduce a robust statistical approach to realization from Minimal Recursion Semantics representations. The approach treats realization as a translation problem, transforming the Dependency MRS graph representation to a surface string. Translation is based on a Synchronous Context-Free Grammar that is automatically extracted from a large corpus of(More)