Persona-based dialogue systems aim to generate consistent responses based on historical context and predefined persona. Unlike conventional dialogue generation, the persona-based dialogue needs to consider both dialogue context and persona, posing a challenge for coherent training. Specifically, this requires a delicate weight balance between context and persona. To achieve that, in this paper, we propose an effective framework with Persona-Adaptive Attention (PAA), which adaptively integrates the weights from the persona and context information via our designed attention. In addition, a dynamic masking mechanism is applied to the PAA to not only drop redundant information in context and persona but also serve as a regularization mechanism to avoid overfitting. Experimental results demonstrate the superiority of the proposed PAA framework compared to the strong baselines in both automatic and human evaluation. Moreover, the proposed PAA approach can perform equivalently well in a low-resource regime compared to models trained in a full-data setting, which achieve a similar result with only 20% to 30% of data compared to the larger models trained in the full-data setting. To fully exploit the effectiveness of our design, we designed several variants for handling the weighted information in different ways, showing the necessity and sufficiency of our weighting and masking designs.

本文提出一种基于人设适应注意力(PAA)的对话生成框架，该框架可在考虑对话上下文和人设的情况下，自适应地整合来自人设和上下文信息的权重。与强基准相比，实验结果表明，所提出的PAA框架在自动和人类评估方面均具有优越性，并且与在全数据设置下训练的大型模型相比，所提出的PAA方法可以在低资源组态下表现出与之相似的结果。

个性化对话生成：基于角色自适应注意力机制