Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation

• URL: http://arxiv.org/abs/2306.10317v1
• Date: Sat, 17 Jun 2023 10:50:19 GMT
• Title: Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation
• Authors: Weihao Zeng, Lulu Zhao, Keqing He, Ruotong Geng, Jingang Wang, Wei Wu, Weiran Xu
• Abstract summary: Existing controllable dialogue generation work focuses on the single-attribute control. We propose a prompt-based disentangled controllable dialogue generation model, DCG.
• Score: 23.79168163871952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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