Before revision:  In this work , we propose a scoring function that is specifically designed to identify low-quality utterance--response pairs to filter noisy training data. Our scoring function models the naturalness of the interconnection within dialogue pairs and their content-relatedness, which is based on previous findings in dialogue response generation and linguistics .
After revision:  In this work , 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 response generation and linguistics .