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Open-domain neural dialogue models have achieved high performance in responseranking and evaluation tasks. These tasks are formulated as a binaryclassification of responses given in a dialogue context, and models generallylearn to make predictions based on context-response content similarity.However, over-reliance on content similarity makes the models less sensitive tothe presence of inconsistencies, incorrect time expressions and other factorsimportant for response appropriateness and coherence. We propose approaches forautomatically creating adversarial negative training data to help ranking andevaluation models learn features beyond content similarity. We proposemask-and-fill and keyword-guided approaches that generate negative examples fortraining more robust dialogue systems. These generated adversarial responseshave high content similarity with the contexts but are either incoherent,inappropriate or not fluent. Our approaches are fully data-driven and can beeasily incorporated in existing models and datasets. Experiments onclassification, ranking and evaluation tasks across multiple datasetsdemonstrate that our approaches outperform strong baselines in providinginformative negative examples for training dialogue systems.

Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation