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A scaled‐down neural conversational model for chatbots
Author(s) -
Mathur Saurabh,
Lopez Daphne
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4761
Subject(s) - conversation , computer science , encode , vocabulary , field (mathematics) , artificial intelligence , quality (philosophy) , natural language processing , machine learning , speech recognition , linguistics , communication , psychology , biochemistry , chemistry , philosophy , mathematics , epistemology , pure mathematics , gene
Summary 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.