Current neural network-based conversational models lack diversity and
generate boring responses to open-ended utterances. Priors such as persona,
emotion, or topic provide additional information to dialog models to aid
response generation, but annotating a dataset with priors is expensive and such
annotations are rarely available. While previous methods for improving the
quality of open-domain response generation focused on either the underlying
model or the training objective, we present a method of filtering dialog
datasets by removing generic utterances from training data using a simple
entropy-based approach that does not require human supervision. We conduct
extensive experiments with different variations of our method, and compare
dialog models across 17 evaluation metrics to show that training on datasets
filtered this way results in better conversational quality as chatbots learn to
output more diverse responses.

采用基于熵的方法，从对话数据集中过滤通用语句，以改善聊天机器人生成开放式回复时的多样性。通过 17 种评估指标的比较，我们证明使用经过此种过滤的数据集训练对话模型可以提高对话质量。