Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings

Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings

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by Kevin Gimpel , Noah A. Smith

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@MISC{Gimpel_cubesumming,,
    author = {Kevin Gimpel and Noah A. Smith},
    title = {Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings},
    year = {}
}

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Abstract

We introduce cube summing, a technique that permits dynamic programming algorithms for summing over structures (like the forward and inside algorithms) to be extended with non-local features that violate the classical structural independence assumptions. It is inspired by cube pruning (Chiang, 2007; Huang and Chiang, 2007) in its computation of non-local features dynamically using scored k-best lists, but also maintains additional residual quantities used in calculating approximate marginals. When restricted to local features, cube summing reduces to a novel semiring (k-best+residual) that generalizes many of the semirings of Goodman (1999). When non-local features are included, cube summing does not reduce to any semiring, but is compatible with generic techniques for solving dynamic programming equations. 1

Citations

1548	BConditional random fields: Probabilistic models for segmenting and labeling sequence data - Lafferty, McCallum, et al.
874	Error bounds for convolutional codes and an asymptotically optimum decoding algorithm - Viterbi - 1967
656	An Introduction to Variational Methods for Graphical Models - Jordan, Ghahramani, et al. - 1999
573	A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains - Baum, Petrie, et al. - 1970
240	Inside-Outside Reestimation from Partially Bracketed Corpora - Pereira, Schabes - 1992
209	Hierarchical phrase-based translation - Chiang - 2007
192	Incorporating nonlocal information into information extraction systems by gibbs sampling - Finkel, Grenager, et al. - 2005
150	Principles and implementation of deductive parsing - Shieber, Schabes, et al. - 1995
111	2006. Online learning of approximate dependency parsing algorithms - McDonald, Pereira
103	Better k-best parsing - Huang, Chiang - 2005
91	A linear programming formulation for global inference in natural language tasks - Roth, Yih - 2004
86	A differential approach to inference in Bayesian networks - Darwiche - 2003
68	Ensemble learning for hidden Markov models - Mackay - 1997
65	Parsing Inside-Out - Goodman - 1998
50	Semiring parsing - Goodman - 1998
47	Dependency parsing by belief propagation - Smith, Eisner - 2008
42	Parsing and hypergraphs - Klein, Manning - 2001
40	The Harpy Speech Recognition System - Lowerre - 1976
30	Forest Rescoring: Faster Decoding with Integrated Language Models - Huang, Chiang - 2007
29	A discriminative latent variable model for statistical machine translation - Blunsom, Cohn, et al. - 2008
29	Automatic Differentiation of Algorithms - Griewank, Corliss, et al. - 1991
27	Stacking dependency parsers - Martins, Das, et al. - 2008
26	Hidden-variable models for discriminative reranking - Koo, Collins - 2005
20	Forest reranking: Discriminative parsing with non-local features - Huang - 2008
19	Modeling the effects of memory on human online sentence processing with particle filters - Levy, Reali, et al. - 2009
18	Stacked sequential learning - Cohen, Carvalho - 2005
16	Probabilistic inference for machine translation - Blunsom, Osborne - 2008
15	Variational Bayesian grammar induction for natural language - Kurihara, Sato - 2006
9	Compiling Comp Ling: Practical weighted dynamic programming and the Dyna language - Eisner, Goldlust, et al. - 2005
3	Computationally efficient M-estimation of log-linear structure models - Smith, Vail, et al. - 2007