通过数据增强与异质对话图网络提升对话中的人格识别能力
Enhancing Personality Recognition in Dialogue by Data Augmentation and Heterogeneous Conversational Graph Networks
AI总结：
针对对话人格识别中语料说话者少、对话依赖关系建模难的问题，提出人格特质插值数据增强法与异质对话图网络，在RealPersonaChat语料上效果显著优于现有基线。
摘要：
人格识别有助于提升机器人生成适配用户的响应的能力，从而丰富人机交互。该任务的挑战之一是现有对话语料库中说话者数量有限，阻碍了鲁棒的、不依赖特定说话者的人格识别模型的发展。此外，准确建模对话中对话者之间的相互依赖关系以及说话者内部的依赖关系仍是一个重要问题。为应对第一个挑战，我们引入人格特质插值法用于说话者数据增强。针对第二个挑战，我们提出异质对话图网络，以独立捕捉语境影响和内在人格特质。在RealPersonaChat语料库上的评估表明，我们的方法相比现有基线有显著提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
