Language Model Size Reduction By Pruning And Clustering

Language Model Size Reduction By Pruning And Clustering (2000)

by Joshua Goodman , Jianfeng Gao

Venue:	In ICSLP’00
Citations:	19 - 6 self

Datum	Value	Source
TITLE	Language Model Size Reduction by Pruning and Clustering	INFERENCE
AUTHOR NAME	Joshua Goodman	user correction - Legacy Corrections
AUTHOR AFFIL	Speech Technology Group; Microsoft Research; Redmond	user correction - Legacy Corrections
AUTHOR ADDR	Washington 98052, USA	user correction - Legacy Corrections
AUTHOR NAME	Jianfeng Gao	user correction - Legacy Corrections
ABSTRACT	Several techniques are known for reducing the size of language models, including count cutoffs [1], Weighted Difference pruning [2], Stolcke pruning [3], and clustering [4]. We compare all of these techniques and show some surprising results. For instance, at low pruning thresholds, Weighted Difference and Stolcke pruning underperform count cutoffs. We then show novel clustering techniques that can be combined with Stolcke pruning to produce the smallest models at a given perplexity. The resulting models can be a factor of three or more smaller than models pruned with Stolcke pruning, at the same perplexity. The technique creates clustered models that are often larger than the unclustered models, but which can be pruned to models that are smaller than unclustered models with the same perplexity.	user correction - Legacy Corrections
YEAR	2000	INFERENCE
VENUE	In ICSLP’00	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	110--113	INFERENCE
CITATIONS	5 found	ParsCit 1.0