Personality recognition is useful for enhancing robots' ability to tailor user-adaptive responses, thus fostering rich human-robot interactions. One of the challenges in this task is a limited number of speakers in existing dialogue corpora, which hampers the development of robust, speaker-independent personality recognition models. Additionally, accurately modeling both the interdependencies among interlocutors and the intra-dependencies within the speaker in dialogues remains a significant issue. To address the first challenge, we introduce personality trait interpolation for speaker data augmentation. For the second, we propose heterogeneous conversational graph networks to independently capture both contextual influences and inherent personality traits. Evaluations on the RealPersonaChat corpus demonstrate our method's significant improvements over existing baselines.

翻译：个性识别有助于增强机器人定制用户适应性响应的能力，从而促进丰富的人机交互。该任务面临的挑战之一是现有对话语料库中说话者数量有限，这阻碍了鲁棒且与说话者无关的个性识别模型的发展。此外，如何在对话中精确建模对话者之间的相互依赖关系以及说话者内部的自依赖关系仍是一个重要问题。针对第一个挑战，我们引入了用于说话者数据增强的个性特征插值方法；针对第二个挑战，我们提出了异构对话图网络，以独立捕捉上下文影响和固有个性特质。在RealPersonaChat语料库上的评估表明，我们的方法相比现有基线取得了显著改进。