Ye-Yi Wang

Decoding Algorithm in Statistical Machine Translation

Decoding algorithm is a crucial part in statistical machine translation. We describe a stack decoding algorithm in this paper. We present the hypothesis scoring method and the heuristics used in our algorithm. We report several techniques deployed to improve the performance of the decoder. We also introduce a simplified model to moderate the sparse data problem and to speed up the decoding process. We evaluate and compare these techniques/models in our statistical machine translation system.

Modeling with Structures in Statistical Machine translation

Most statistical machine translation systems employ a word-based alignment model. In this paper we demonstrate that word-based alignment is a major cause of translation errors. We propose a new alignment model based on shallow phrase structures, and the structures can be automatically acquired from parallel corpus. This new model achieved over 10% error reduction for our spoken language translation task.

Word clustering with parallel spoken language corpora

We introduce a word clustering algorithm which uses a bilingual, parallel corpus to group together words in the source and target language. Our method generalizes previous mutual information clustering algorithms for monolingual data by incorporating a statistical translation model. Preliminary experiments have shown that the algorithm can effectively employ the constraints implicit in bilingual data to extract classes which are well suited to machine translation tasks.

Improved language modelling by unsupervised acquisition of structure

The perplexity of corpora is typically reduced by more than 30% compared to advanced n-gram models by a new method for the unsupervised acquisition of structural text models. This method is based on new algorithms for the classification of words and phrases from context and on new sequence finding procedures. These procedures are designed to work fast and accurately on small and large corpora. They are iterated to build a structural model of a corpus. The structural model can be applied to recalculate the scores of a speech recogniser and improves the word accuracy. Further applications such as preprocessing for neural networks and (hidden) Markov models in language processing, which exploit the structure finding capabilities of this model, are proposed.

A connectionist model for dialog processing

A novel connectionist system for dialog processing is described. Based on a script-like formalism, the system consists of several modular neural networks which can track the semantic flow of a dialog. The system can be extended to understand and translate dialogs in a certain domain.<<ETX>>

DUAL-CODING THEORY AND CONNECTIONIST LEXICAL SELECTION

We introduce the bilingual dual-coding theory as a model for bilingual mental representation. Based on this model, lexical selection neural networks are implemented for a connectionist transfer project in machine translation.