End to End Dialogue Transformer

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.

References in corpus (5)

• Language Models are Few-Shot Learners
• MASS: Masked Sequence to Sequence Pre-training for Language Generation
• Incorporating Copying Mechanism in Sequence-to-Sequence Learning
• A Network-based End-to-End Trainable Task-oriented Dialogue System
• Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models