@inproceedings{zeng-etal-2023-seen,
    title = "Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation",
    author = "Zeng, Weihao  and
      Zhao, Lulu  and
      He, Keqing  and
      Geng, Ruotong  and
      Wang, Jingang  and
      Wu, Wei  and
      Xu, Weiran",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.793",
    doi = "10.18653/v1/2023.acl-long.793",
    pages = "14179--14196",
    abstract = "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.",
}

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.
Zeng, Weihao Zhao, Lulu He, Keqing Geng, Ruotong Wang, Jingang Wu, Wei Xu, Weiran
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation