Heuristic search for non-bottom-up tree structure prediction

Authors:
Andrea Gesmundo;James Henderson
Affiliations:
University of Geneva;University of Geneva
Venue:
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Year:
2011

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Abstract

State of the art Tree Structures Prediction techniques rely on bottom-up decoding. These approaches allow the use of context-free features and bottom-up features. We discuss the limitations of mainstream techniques in solving common Natural Language Processing tasks. Then we devise a new framework that goes beyond Bottom-up Decoding, and that allows a better integration of contextual features. Furthermore we design a system that addresses these issues and we test it on Hierarchical Machine Translation, a well known tree structure prediction problem. The structure of the proposed system allows the incorporation of non-bottom-up features and relies on a more sophisticated decoding approach. We show that the proposed approach can find better translations using a smaller portion of the search space.