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A scaled‐down neural conversational model for chatbots
Mathur S,
Published in Wiley
2019
Volume: 31
   
Issue: 10
Abstract

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.

About the journal
JournalData powered by TypesetConcurrency and Computation: Practice and Experience
PublisherData powered by TypesetWiley
ISSN1532-0626
Open Access0