  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.
