Automatic evaluating the performance of Open-domain dialogue system is a
challenging problem. Recent work in neural network-based metrics has shown
promising opportunities for automatic dialogue evaluation. However, existing
methods mainly focus on monolingual evaluation, in which the trained metric is
not flexible enough to transfer across different languages. To address this
issue, we propose an adversarial multi-task neural metric (ADVMT) for
multi-lingual dialogue evaluation, with shared feature extraction across
languages. We evaluate the proposed model in two different languages.
Experiments show that the adversarial multi-task neural metric achieves a high
correlation with human annotation, which yields better performance than
monolingual ones and various existing metrics.

提出了一种跨语言对话评估的 ADVMT 模型，利用神经网络指标对开放域对话系统的表现进行评估，并在两种不同语言的情况下进行评估，结果显示其与人工评估的相关性高于现有指标。