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

By Lu Li, Zhongheng He, Xiangyang Zhou, Dianhai Yu. 2019

3 citations recorded by the source corpus.

Automatic dialogue evaluation plays a crucial role in open-domain dialogue research. Previous works train neural networks with limited annotation for conducting automatic dialogue evaluation, which would naturally affect the evaluation fairness as dialogue systems close to the scope of training corpus would have more preference than the other ones. In this paper, we study alleviating this problem from the perspective of continual learning: given an existing neural dialogue evaluator and the next system to be evaluated, we fine-tune the learned neural evaluator by selectively forgetting/updating its parameters, to jointly fit dialogue systems have been and will be evaluated. Our motivation is to seek for a lifelong and low-cost automatic evaluation for dialogue systems, rather than to reconstruct the evaluator over and over again. Experimental results show that our continual evaluator achieves comparable performance with reconstructing new evaluators, while requires significantly lower resources.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Continual Learning in Task-Oriented Dialogue Systems (2020, methodology): “Of this kind, multiple models has been proposed such as Progressive Net (Rusu et al., 2016), Dynamically Expandable Networks (DEN) (Yoon et al., 2017) and Learnto-Grow (Li et al., 2019b).”