End-to-end Adversarial Learning for Generative Conversational Agents

Understand

This paper presents a new adversarial learning method for generative conversational agents (GCA) besides a new model of GCA.

• Similar to previous works on adversarial learning for dialogue generation, our method assumes the GCA as a generator that aims at fooling a discriminator that labels dialogues as human-generated or machine-generated; however, in our approach, the discriminator performs token-level classification, i.e.
• it indicates whether the current token was generated by humans or machines.
• To do so, the discriminator also receives the context utterances (the dialogue history) and the incomplete answer up to the current token as input.