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Open Domain dialog system evaluation is one of the most important challengesin dialog research. Existing automatic evaluation metrics, such as BLEU aremostly reference-based. They calculate the difference between the generatedresponse and a limited number of available references. Likert-score basedself-reported user rating is widely adopted by social conversational systems,such as Amazon Alexa Prize chatbots. However, self-reported user rating suffersfrom bias and variance among different users. To alleviate this problem, weformulate dialog evaluation as a comparison task. We also propose an automaticevaluation model CMADE (Comparison Model for Automatic Dialog Evaluation) thatautomatically cleans self-reported user ratings as it trains on them.Specifically, we first use a self-supervised method to learn better dialogfeature representation, and then use KNN and Shapley to remove confusingsamples. Our experiments show that CMADE achieves 89.2% accuracy in the dialogcomparison task.

Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation