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Deep learning has revolutionized the field of conversation modeling. A lot of the research has been toward making the conversational agent more human-like. As a result, overall the model size increases. Bigger models require more data and are costly to build and maintain. Often, for some tasks, high-quality responses are not necessary. In this paper, a model that consumes fewer resources and a way to augment conversation data without increasing the size of the vocabulary is proposed. The proposed model uses a modified version of the GRU instead of the LSTM to encode and decode sequences of text. © 2018 John Wiley & Sons, Ltd.
Journal | Data powered by TypesetConcurrency and Computation: Practice and Experience |
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Publisher | Data powered by TypesetWiley |
ISSN | 1532-0626 |
Open Access | 0 |