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.

翻译：基于人物特征的对话系统旨在根据历史上下文和预定义人物特征生成一致的回复。与常规对话生成不同，基于人物特征的对话需要同时考虑对话上下文和人物特征，这对协同训练构成了挑战。具体而言，这需要在上下文与人物特征之间实现精细的权重平衡。为此，本文提出了一种包含人物自适应注意力（PAA）的有效框架，该框架通过设计的注意力机制自适应地整合人物特征与上下文信息的权重。此外，我们对PAA应用动态掩码机制，不仅能够剔除上下文和人物特征中的冗余信息，还能作为正则化机制避免过拟合。实验结果表明，在自动评估与人工评估中，所提PAA框架相较于强基线方法均展现出优越性。更重要的是，在低资源场景下，所提PAA方法的表现与全数据训练的模型相当——仅需全数据训练模式下20%至30%的数据即可达到与大型模型相近的结果。为充分验证设计的有效性，我们设计了多种变体以不同方式处理加权信息，进一步证明了权重分配与掩码设计的必要性与充分性。