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DISTRIBUTED TRAINING OF LARGE SCALE EXPONENTIAL LANGUAGE MODELS

Full Paper at IEEE Xplore

Language Modeling

Presented by: Bhuvana Ramabhadran, Author(s): Abhinav Sethy, Stanley Chen, Bhuvana Ramabhadran, IBM, United States

Shrinkage-based exponential language models, such as the recently introduced Model M, have provided significant gains over a range of tasks . Training such models requires a large amount of computational resources in terms of both time and memory. In this paper, we present a distributed training algorithm for such models based on the idea of cluster expansion . Cluster expansion allows us to efficiently calculate the normalization and expectations terms required for Model M training by minimizing the computation needed between consecutive n-grams. We also show how the algorithm can be implemented in a distributed environment, greatly reducing the memory required per process and training time.

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  Lecture Information

Recorded: 2011-05-25 16:35 - 16:55, Club H
Added: 9. 6. 2011 01:58
Number of views: 47
Video resolution: 1024x576 px, 512x288 px
Video length: 0:19:16
Audio track: MP3 [6.58 MB], 0:19:16