Personality Recognition in Conversation

1 papers with code • 1 benchmarks • 1 datasets

Given a speaker's conversation with others, it is required to recognize the speaker's personality traits through the conversation record, which includes two scenarios, (1) $1-1$ conversations: the robot recognizes the personality traits of the speaker through the conversation between them (e.g., psychological counseling), (2) $1-N$ conversations : the robot listens to the speaker's conversations with other $N$ people and then recognizes the speaker's personality traits (e.g., group chatbot, home service robot). Since $1-N$ includes the case of $1-1$, we only discusses PRC in $1-N$ conversations. The task of PRC in $1-N$ conversations can be formulated as:

$Per_i = argmax_{Per'i}P(Per'_i | C{i,j}, \cdots, C_{i,N})$

where $Per_i=[Neu, Ext, Ope, Agr, Con]$ is a 5-dimensional vector representing Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. $C_{i,j}$ is the conversations between $Speaker_i$ and $Speaker_j$ ($1 \leq j \leq N$).

CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI

29 May 2022

Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation.

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