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

• URL: http://arxiv.org/abs/2005.10716v2
• Date: Fri, 12 Jun 2020 04:05:58 GMT
• Title: Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation
• Authors: Weixin Liang, James Zou, Zhou Yu
• Abstract summary: Open Domain dialog system evaluation is one of the most important challenges in dialog research. We propose an automatic evaluation model CMADE that automatically cleans self-reported user ratings as it trains on them. Our experiments show that CMADE achieves 89.2% accuracy in the dialog comparison task.
• Score: 69.03658685761538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open Domain dialog system evaluation is one of the most important challenges in dialog research. Existing automatic evaluation metrics, such as BLEU are mostly reference-based. They calculate the difference between the generated response and a limited number of available references. Likert-score based self-reported user rating is widely adopted by social conversational systems, such as Amazon Alexa Prize chatbots. However, self-reported user rating suffers from bias and variance among different users. To alleviate this problem, we formulate dialog evaluation as a comparison task. We also propose an automatic evaluation model CMADE (Comparison Model for Automatic Dialog Evaluation) that automatically cleans self-reported user ratings as it trains on them. Specifically, we first use a self-supervised method to learn better dialog feature representation, and then use KNN and Shapley to remove confusing samples. Our experiments show that CMADE achieves 89.2% accuracy in the dialog comparison task.

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