In empathetic conversations, individuals express their empathy towards
others. Previous work has mainly focused on generating empathetic responses by
utilizing the speaker's emotion. Besides, external commonsense knowledge has
been applied to enhance the system's understandings of the speaker's situation.
However, given an event, commonsense knowledge base contains various relations,
potentially leading to confusion for the dialogue system. Consequently,
inconsistencies arise among the emotion, generated response and speaker's
contextual information. To this end, we propose a novel approach for empathetic
response generation, which incorporates an adaptive module for commonsense
knowledge selection to ensure consistency between the generated empathetic
responses and the speaker's situation. This selected knowledge is used to
refine the commonsense cognition and empathy expression for generated
responses. Experimental results show that our approach significantly
outperforms baseline models in both automatic and human evaluations, exhibiting
the generation of more coherent and empathetic responses. Moreover, case
studies highlight the interpretability of knowledge selection in the responses
and the effectiveness of adaptive module in our model. Code:
this https URL

本文提出了一种新颖的共情回应生成方法，该方法将自适应模块应用于常识知识选择，以确保生成的共情回应与说话者的情境一致，并使用所选知识来完善常识认知和共情表达以生成更一致和共情的回应。实验结果表明，与基准模型相比，我们的方法在自动和人类评估方面都显着优于基线模型，生成更一致和共情的回应。此外，案例研究强调了响应中知识选择的可解释性以及自适应模块在我们的模型中的有效性。

动态注入常识知识提高共情式对话生成效果

Improving Empathetic Dialogue Generation by Dynamically Infusing  Commonsense Knowledge

We propose a precise and efficient normal estimation method that can deal
with noise and nonuniform density for unstructured 3D point clouds. Unlike
existing approaches that directly take patches and ignore the local
neighborhood relationships, which make them susceptible to challenging regions
such as sharp edges, we propose to learn graph convolutional feature
representation for normal estimation, which emphasizes more local neighborhood
geometry and effectively encodes intrinsic relationships. Additionally, we
design a novel adaptive module based on the attention mechanism to integrate
point features with their neighboring features, hence further enhancing the
robustness of the proposed normal estimator against point density variations.
To make it more distinguishable, we introduce a multi-scale architecture in the
graph block to learn richer geometric features. Our method outperforms
competitors with the state-of-the-art accuracy on various benchmark datasets,
and is quite robust against noise, outliers, as well as the density variations.

本文提出了一种精确高效的方法，用于处理非结构化三维点云上的噪声与非均匀密度，并通过图卷积特征表示学习局部邻域几何信息进行法向量估计，设计了基于注意力机制的自适应模块，进一步增强了法向估计器对点密度变化的鲁棒性，并引入多尺度结构提取图块来学习更丰富的几何特征，在多个基准数据集上实现了最先进的精度和较强的噪声鲁棒性。