PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative Dialogue Systems

• URL: http://arxiv.org/abs/2004.02399v1
• Date: Mon, 6 Apr 2020 04:36:33 GMT
• Title: PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative Dialogue Systems
• Authors: Tian Lan, Xian-Ling Mao, Wei Wei, Xiaoyan Gao, Heyan Huang
• Abstract summary: There are three kinds of automatic methods to evaluate the open-domain generative dialogue systems. Due to the lack of systematic comparison, it is not clear which kind of metrics are more effective. We propose a novel and feasible learning-based metric that can significantly improve the correlation with human judgments.
• Score: 48.99561874529323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-domain generative dialogue systems have attracted considerable attention over the past few years. Currently, how to automatically evaluate them, is still a big challenge problem. As far as we know, there are three kinds of automatic methods to evaluate the open-domain generative dialogue systems: (1) Word-overlap-based metrics; (2) Embedding-based metrics; (3) Learning-based metrics. Due to the lack of systematic comparison, it is not clear which kind of metrics are more effective. In this paper, we will first measure systematically all kinds of automatic evaluation metrics over the same experimental setting to check which kind is best. Through extensive experiments, the learning-based metrics are demonstrated that they are the most effective evaluation metrics for open-domain generative dialogue systems. Moreover, we observe that nearly all learning-based metrics depend on the negative sampling mechanism, which obtains an extremely imbalanced and low-quality dataset to train a score model. In order to address this issue, we propose a novel and feasible learning-based metric that can significantly improve the correlation with human judgments by using augmented POsitive samples and valuable NEgative samples, called PONE. Extensive experiments demonstrate that our proposed evaluation method significantly outperforms the state-of-the-art learning-based evaluation methods, with an average correlation improvement of 13.18%. In addition, we have publicly released the codes of our proposed method and state-of-the-art baselines.

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