  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.
