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SotA/Natural Language Processing/Personality Recognition in Conversation

Personality Recognition in Conversation

7 benchmarks3 papers

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) 1−11-11−1 conversations: the robot recognizes the personality traits of the speaker through the conversation between them (e.g., psychological counseling), (2) 1−N1-N1−N conversations : the robot listens to the speaker's conversations with other NNN people and then recognizes the speaker's personality traits (e.g., group chatbot, home service robot). Since 1−N1-N1−N includes the case of 1−11-11−1, we only discusses PRC in 1−N1-N1−N conversations. The task of PRC in 1−N1-N1−N conversations can be formulated as:

Peri=argmaxPeri′P(Peri′∣Ci,j,⋯ ,Ci,N)Per_i = argmax_{Per'_i}P(Per'_i | C_{i,j}, \cdots, C_{i,N})Peri​=argmaxPeri′​​P(Peri′​∣Ci,j​,⋯,Ci,N​)

where Peri=[Neu,Ext,Ope,Agr,Con]Per_i=[Neu, Ext, Ope, Agr, Con]Peri​=[Neu,Ext,Ope,Agr,Con] is a 5-dimensional vector representing Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. Ci,jC_{i,j}Ci,j​ is the conversations between SpeakeriSpeaker_iSpeakeri​ and SpeakerjSpeaker_jSpeakerj​ (1≤j≤N1 \leq j \leq N1≤j≤N).

Benchmarks

Personality Recognition in Conversation on CPED

Accuracy (%)Macro-F1Accuracy of NeurotismAccuracy of ExtraversionAccuracy of OpennessAccuracy of AgreeablenessAccuracy of Conscientiousness