Distributed word clustering for large scale class-based language modeling in machine translation

Distributed word clustering for large scale class-based language modeling in machine translation (2008)

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by Jakob Uszkoreit , Thorsten Brants

Venue:	In ACL International Conference Proceedings
Citations:	5 - 1 self

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Datum	Value	Source
TITLE	Distributed word clustering for large scale class-based language modeling in machine translation	INFERENCE
AUTHOR NAME	Jakob Uszkoreit	SVM HeaderParse 0.2
AUTHOR AFFIL	Google, Inc.	SVM HeaderParse 0.2
AUTHOR ADDR	1600 Amphitheatre Parkway; Mountain View, CA 94303, USA	SVM HeaderParse 0.2
AUTHOR NAME	Thorsten Brants	SVM HeaderParse 0.2
AUTHOR AFFIL	Google, Inc.	SVM HeaderParse 0.2
AUTHOR ADDR	1600 Amphitheatre Parkway; Mountain View, CA 94303, USA	SVM HeaderParse 0.2
ABSTRACT	In statistical language modeling, one technique to reduce the problematic effects of data sparsity is to partition the vocabulary into equivalence classes. In this paper we investigate the effects of applying such a technique to higherorder n-gram models trained on large corpora. We introduce a modification of the exchange clustering algorithm with improved efficiency for certain partially class-based models and a distributed version of this algorithm to efficiently obtain automatic word classifications for large vocabularies (>1 million words) using such large training corpora (>30 billion tokens). The resulting clusterings are then used in training partially class-based language models. We show that combining them with wordbased n-gram models in the log-linear model of a state-of-the-art statistical machine translation system leads to improvements in translation quality as indicated by the BLEU score. 1	SVM HeaderParse 0.2
YEAR	2008	INFERENCE
VENUE	In ACL International Conference Proceedings	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	14 found	ParsCit 1.0