Generalized interpolation in decision tree LM

Authors:
Denis Filimonov;Mary Harper
Affiliations:
Johns Hopkins University and University of Maryland, College Park;University of Maryland, College Park
Venue:
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Year:
2011

Citing 3
Cited 1

A tree-based statistical language model for natural language speech recognition

Readings in speech recognition
Self-training PCFG grammars with latent annotations across languages

EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
A joint language model with fine-grain syntactic tags

EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3

Syntactic decision tree LMs: random selection or intelligent design?

EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the face of sparsity, statistical models are often interpolated with lower order (backoff) models, particularly in Language Modeling. In this paper, we argue that there is a relation between the higher order and the backoff model that must be satisfied in order for the interpolation to be effective. We show that in n-gram models, the relation is trivially held, but in models that allow arbitrary clustering of context (such as decision tree models), this relation is generally not satisfied. Based on this insight, we also propose a generalization of linear interpolation which significantly improves the performance of a decision tree language model.