  Large-scale dialogue datasets have recently become available for training
neural dialogue agents. However, these datasets have been reported to contain a
non-negligible number of unacceptable utterance pairs. In this paper, we
propose a method for scoring the quality of utterance pairs in terms of their
connectivity and relatedness. The proposed scoring method is designed based on
findings widely shared in the dialogue and linguistics research communities. We
demonstrate that it has a relatively good correlation with the human judgment
of dialogue quality. Furthermore, the method is applied to filter out
potentially unacceptable utterance pairs from a large-scale noisy dialogue
corpus to ensure its quality. We experimentally confirm that training data
filtered by the proposed method improves the quality of neural dialogue agents
in response generation.
