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arXiv · 1511.01158

Distributed Deep Learning for Question Answering

Minwei Feng·Bing Xiang·Bowen Zhou
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Abstract

This paper is an empirical study of the distributed deep learning for question answering subtasks: answer selection and question classification. Comparison studies of SGD, MSGD, ADADELTA, ADAGRAD, ADAM/ADAMAX, RMSPROP, DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results show that the distributed framework based on the message passing interface can accelerate the convergence speed at a sublinear scale. This paper demonstrates the importance of distributed training. For example, with 48 workers, a 24x speedup is achievable for the answer selection task and running time is decreased from 138.2 hours to 5.81 hours, which will increase the productivity significantly.

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Minwei Feng, Bing Xiang, Bowen Zhou. 2016-08-04. Distributed Deep Learning for Question Answering. https://doi.org/10.1145/2983323.2983377

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