Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of single-turn evaluation, do not capture conversation quality in a realistic interactive context. In this paper, we investigate interactive human evaluation and provide evidence for its necessity; we then introduce a novel, model-agnostic, and dataset-agnostic method to approximate it. In particular, we propose a self-play scenario where the dialog system talks to itself and we calculate a combination of proxies such as sentiment and semantic coherence on the conversation trajectory. We show that this metric is capable of capturing the human-rated quality of a dialog model better than any automated metric known to-date, achieving a significant Pearson correlation (r>.7, p<.05). To investigate the strengths of this novel metric and interactive evaluation in comparison to state-of-the-art metrics and one-turn evaluation, we perform extended experiments with a set of models, including several that make novel improvements to recent hierarchical dialog generation architectures through sentiment and semantic knowledge distillation on the utterance level. Finally, we open-source the interactive evaluation platform we built and the dataset we collected to allow researchers to efficiently deploy and evaluate generative dialog models.
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Code to support training, evaluating and interacting neural network dialog models, and training them with reinforcement learning. Code to deploy a web server which hosts the models live online is available at: https://github.com/asmadotgh/neural_chat_web
The server portion of the Neural Chat project to deploy chatbots on web. This code is accompanied by another repository that includes the chatbot models: for training, evaluating and interacting with our open-sourced neural dialog models, use https://github.com/natashamjaques/neural_chat.
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