Compiling Comp Ling: practical weighted dynamic programming and the Dyna language

Authors:
Jason Eisner;Eric Goldlust;Noah A. Smith
Affiliations:
Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD
Venue:
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Year:
2005

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Abstract

Weighted deduction with aggregation is a powerful theoretical formalism that encompasses many NLP algorithms. This paper proposes a declarative specification language, Dyna; gives general agenda-based algorithms for computing weights and gradients; briefly discusses Dyna-to-Dyna program transformations; and shows that a first implementation of a Dyna-to-C++ compiler produces code that is efficient enough for real NLP research, though still several times slower than hand-crafted code.