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

Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery