In this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDIALOG curated in our work. The cur rent models indeed suffer from spurious correlations and have a tendency of generating irrelevant and generic responses. Inspired by causal discovery algorithms, we propose a novel model-agnostic method for training and inference of response generation model using a conditional independence classifier. The classifier is trained by a constrained self-training method, coined CONSTRAIN, to overcome data scarcity. The experimental results based on both human and automatic evaluation show that our method significantly outperforms the competitive baselines in terms of relevance, informativeness, and fluency.

翻译：本文基于我们构建的CGDIALOG语料库，首次研究了开放域响应生成模型中的虚假相关性问题。现有模型确实受到虚假相关性的困扰，倾向于生成无关且泛化的响应。受因果发现算法启发，我们提出了一种新颖的模型无关方法，使用条件独立性分类器进行响应生成模型的训练和推理。该分类器通过名为CONSTRAIN的约束自训练方法进行训练，以克服数据稀缺问题。基于人工评估和自动评估的实验结果表明，我们的方法在相关性、信息量和流畅性方面显著优于竞争性基线模型。