Building an open-domain conversational agent is a challenging problem.
Current evaluation methods, mostly post-hoc judgments of static conversation,
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 human evaluation of static conversations, 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 dialog
models.

本文提出一种交互式的人工评估对话质量的方法，并介绍了一种基于自我对话的度量方式，该方式可以更好地捕捉对话模型的质量，同时使用了情感和语义连贯性等维度。通过对多个模型的实验比较，研究表明，这种度量方式优于目前已知的所有自动化方法，同时也优于静态会话的人工评估。最后，开放性地共享出研究基于交互式评估所构建的数据集和平台以供其它研究者使用。