An Adversarial Learning Framework for a Persona-based Multi-turn Dialogue Model
In this paper, we extend the persona-based sequence-to-sequence 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 and a conditional discriminator.We also explore two approaches to accomplish the conditional discriminator:, a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and, a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute that generated the input utterance.To demonstrate the superior performance of phredGAN over the persona SeqSeq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus 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 on datasets with many but weak attribute modalities and ones with few but strong attribute modalities.
This paper develops an adversarial learning framework for neural conversation models with personaThis paper proposes an extension to hredGAN to simultaneously learn a set of attribute embeddings that represent the persona of each speaker and generate persona-based responses