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• W2146183716 abstract "Shrinkage-based exponential language models, such as the recently introduced Model M, have provided significant gains over a range of tasks [1]. 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 [2]. 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." @default.
• W2146183716 created "2016-06-24" @default.
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• W2146183716 date "2011-05-01" @default.
• W2146183716 modified "2023-09-23" @default.
• W2146183716 title "Distributed training of large scale exponential language models" @default.
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• W2146183716 doi "https://doi.org/10.1109/icassp.2011.5947609" @default.
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