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.
@article{arxiv.2306.10317,
title = {Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation},
author = {Weihao Zeng and Lulu Zhao and Keqing He and Ruotong Geng and Jingang Wang and Wei Wu and Weiran Xu},
journal= {arXiv preprint arXiv:2306.10317},
year = {2023}
}