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Automatic dialogue evaluation plays a crucial role in open-domain dialogueresearch. Previous works train neural networks with limited annotation forconducting automatic dialogue evaluation, which would naturally affect theevaluation fairness as dialogue systems close to the scope of training corpuswould have more preference than the other ones. In this paper, we studyalleviating this problem from the perspective of continual learning: given anexisting neural dialogue evaluator and the next system to be evaluated, wefine-tune the learned neural evaluator by selectively forgetting/updating itsparameters, to jointly fit dialogue systems have been and will be evaluated.Our motivation is to seek for a lifelong and low-cost automatic evaluation fordialogue systems, rather than to reconstruct the evaluator over and over again.Experimental results show that our continual evaluator achieves comparableperformance with reconstructing new evaluators, while requires significantlylower resources.

How to Evaluate the Next System: Automatic Dialogue Evaluation from the Perspective of Continual Learning