Seen to Unseen: Exploring Compositional Generalization of
  Multi-Attribute Controllable Dialogue Generation
Weihao Zeng Lulu Zhao Keqing He Ruotong Geng Jingang Wang Wei Wu Weiran Xu
Existing controllable dialogue generation work focuses on the
single-attribute control and lacks generalization capability to
out-of-distribution multiple attribute combinations. In this paper, we explore
the compositional generalization for multi-attribute controllable dialogue
generation where a model can learn from seen attribute values and generalize to
unseen combinations. We propose a prompt-based disentangled controllable
dialogue generation model, DCG. It learns attribute concept composition by
generating attribute-oriented prompt vectors and uses a disentanglement loss to
disentangle different attributes for better generalization. Besides, we design
a unified reference-free evaluation framework for multiple attributes with
different levels of granularities. Experiment results on two benchmarks prove
the effectiveness of our method and the evaluation metric.