Affective-NLI: Towards Accurate and Interpretable Personality
  Recognition in Conversation
Zhiyuan Wen Jiannong Cao Yu Yang Ruosong Yang Shuaiqi Liu
Personality Recognition in Conversation (PRC) aims to identify the
personality traits of speakers through textual dialogue content. It is
essential for providing personalized services in various applications of
Human-Computer Interaction (HCI), such as AI-based mental therapy and companion
robots for the elderly. Most recent studies analyze the dialog content for
personality classification yet overlook two major concerns that hinder their
performance. First, crucial implicit factors contained in conversation, such as
emotions that reflect the speakers' personalities are ignored. Second, only
focusing on the input dialog content disregards the semantic understanding of
personality itself, which reduces the interpretability of the results. In this
paper, we propose Affective Natural Language Inference (Affective-NLI) for
accurate and interpretable PRC. To utilize affectivity within dialog content
for accurate personality recognition, we fine-tuned a pre-trained language
model specifically for emotion recognition in conversations, facilitating
real-time affective annotations for utterances. For interpretability of
recognition results, we formulate personality recognition as an NLI problem by
determining whether the textual description of personality labels is entailed
by the dialog content. Extensive experiments on two daily conversation datasets
suggest that Affective-NLI significantly outperforms (by 6%-7%)
state-of-the-art approaches. Additionally, our Flow experiment demonstrates
that Affective-NLI can accurately recognize the speaker's personality in the
early stages of conversations by surpassing state-of-the-art methods with
22%-34%.