In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.
Ranked #1 on Dialogue Generation on Amazon-5
Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem.
Capitalized on the topic-level dialogue graph, we propose a new evaluation metric GRADE, which stands for Graph-enhanced Representations for Automatic Dialogue Evaluation.
Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue.
To overcome the limitations of automated metrics (e. g. BLEU, METEOR) for evaluating dialogue systems, researchers typically use human judgments to provide convergent evidence.
To alleviate this risk, we propose an adversarial training approach to learn a robust model, ATT (Adversarial Turing Test), that discriminates machine-generated responses from human-written replies.