NDLI: Large-Scale Distributed Language Modeling

Content Provider	IEEE Xplore Digital Library
Author	Emami, A. Papineni, K. Sorensen, J.
Copyright Year	2007
Description	Author affiliation: IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA (Emami, A.)
Abstract	A novel distributed language model that has no constraints on the n-gram order and no practical constraints on vocabulary size is presented. This model is scalable and allows for an arbitrarily large corpus to be queried for statistical estimates. Our distributed model is capable of producing n-gram counts on demand. By using a novel heuristic estimate for the interpolation weights of a linearly interpolated model, it is possible to dynamically compute the language model probabilities. The distributed architecture follows the client-server paradigm and allows for each client to request an arbitrary weighted mixture of the corpus. This allows easy adaptation of the language model to particular test conditions. Experiments using the distributed LM for re-ranking N-best lists of a speech recognition system resulted in considerable improvements in word error rate (WER), while integration with a machine translation decoder resulted in significant improvements in translation quality as measured by the BLEU score.
File Size	5056397
File Format	PDF
ISBN	1424407273
ISSN	15206149
DOI	10.1109/ICASSP.2007.367157
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Large-scale systems Decoding Natural languages Training data Automatic speech recognition Vocabulary Probability Speech recognition Error analysis Surface-mount technology Distributed memory systems Statistical language modeling Statistical machine translation Client-server systems
Content Type	Text
Resource Type	Article

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