  In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq)
neural network conversation model to a multi-turn dialogue scenario by
modifying the state-of-the-art hredGAN architecture to simultaneously capture
utterance attributes such as speaker identity, dialogue topic, speaker
sentiments and so on. The proposed system, phredGAN has a persona-based HRED
generator (PHRED) and a conditional discriminator. We also explore two
approaches to accomplish the conditional discriminator: (1) phredGAN_a, a
system that passes the attribute representation as an additional input into a
traditional adversarial discriminator, and (2) phredGAN_d, a dual discriminator
system which in addition to the adversarial discriminator, collaboratively
predicts the attribute(s) that generated the input utterance. To demonstrate
the superior performance of phredGAN over the persona Seq2Seq model, we
experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC)
and TV series transcripts from the Big Bang Theory and Friends. Performance
comparison is made with respect to a variety of quantitative measures as well
as crowd-sourced human evaluation. We also explore the trade-offs from using
either variant of phredGAN on datasets with many but weak attribute modalities
(such as with Big Bang Theory and Friends) and ones with few but strong
attribute modalities (customer-agent interactions in Ubuntu dataset).
