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Computer Science @ Rochester
Friday, May 11, 2007
2:00 PM
Computer Science Bldg. Room 601
Ph.D. Thesis Proposal
Ding Liu
University of Rochester
"Statistical Semantic based Machine Translation"
Statistical Syntax based Machine Translation (MT) has been well developed and achieved competitive performance as the state-of-art phrase based MT systems. It is due to our better understanding of the synchronous grammars and better techniques on searching and pruning. As we all know, semantics is a more general structure built upon the syntax, and the semantic roles show the functionality of the syntactic constituents with respect to a certain predicate. With the techniques for the syntax based MT, we cannot help but wonder: can we go one step further and make a statistical semantic based MT sytem?

In this proposal, we address the potential benefit of using semantic features in a MT system, and the possible ways of building a statistical semantic based MT system upon a syntax based MT system. The tree-to-string template approach to the syntax based MT provides a good framework to incorporate the semantic information, with which our semantic based MT can be divided into two steps: first, extract the semantic role information from the syntax trees, second, use the semantic features to modify the syntax MT models. We propose several methods to integrate the semantic role features into a tree-to-string MT system, such as enriching the tree node labels with the semantic role labels, constructing versatile semantic features for the discriminative MT models, and making a two-layer translation model with the semantic structure.

We also explore the new methods for the manual/automatic evaluation of semantic based MT. The motivation is that the semantic features can be used to divide the evaluation task into two parts: the skeleton evaluation focusing on fluency, and the arguments evaluation focusing on adequacy. Such division makes the manual evaluation more managable and provides the automatic evaluation features in semantic level.