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End to End Dialogue Transformer

2020-08-24 12:43:08
Ondřej Měkota, Memduh Gökırmak, Petr Laitoch

Abstract

Dialogue systems attempt to facilitate conversations between humans and computers, for purposes as diverse as small talk to booking a vacation. We are here inspired by the performance of the recurrent neural network-based model Sequicity, which when conducting a dialogue uses a sequence-to-sequence architecture to first produce a textual representation of what is going on in the dialogue, and in a further step use this along with database findings to produce a reply to the user. We here propose a dialogue system based on the Transformer architecture instead of Sequicity's RNN-based architecture, that works similarly in an end-to-end, sequence-to-sequence fashion.

Abstract (translated)

URL

https://arxiv.org/abs/2008.10392

PDF

https://arxiv.org/pdf/2008.10392.pdf


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