End to End Dialogue Transformer

• URL: http://arxiv.org/abs/2008.10392v1
• Date: Mon, 24 Aug 2020 12:43:08 GMT
• Title: End to End Dialogue Transformer
• Authors: Ond\v{r}ej M\v{e}kota, Memduh G\"ok{\i}rmak, Petr Laitoch
• Abstract summary: We are inspired by the performance of the recurrent neural network-based model Sequicity. We propose a dialogue system based on the Transformer architecture instead of Sequicity's RNN-based architecture.
• Score: 0.0019832631155284838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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