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Large-scale dialogue datasets have recently become available for trainingneural dialogue agents. However, these datasets have been reported to contain anon-negligible number of unacceptable utterance pairs. In this paper, wepropose a method for scoring the quality of utterance pairs in terms of theirconnectivity and relatedness. The proposed scoring method is designed based onfindings widely shared in the dialogue and linguistics research communities. Wedemonstrate that it has a relatively good correlation with the human judgmentof dialogue quality. Furthermore, the method is applied to filter outpotentially unacceptable utterance pairs from a large-scale noisy dialoguecorpus to ensure its quality. We experimentally confirm that training datafiltered by the proposed method improves the quality of neural dialogue agentsin response generation.

Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness