Personality Recognition In Conversation On 1
Metriken
Accuracy (%)
Accuracy of Agreeableness
Accuracy of Conscientiousness
Accuracy of Extraversion
Accuracy of Neurotism
Accuracy of Openness
Macro-F1
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy (%) | Accuracy of Agreeableness | Accuracy of Conscientiousness | Accuracy of Extraversion | Accuracy of Neurotism | Accuracy of Openness | Macro-F1 | Paper Title | Repository |
|---|---|---|---|---|---|---|---|---|---|
| BERT$_{ssenet}^{c}$ | 67.25 | 85.89 | 63.48 | 78.21 | 53.27 | 55.42 | 74.08 | CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI | |
| BERT$^{s}$ | 67.23 | 85.76 | 63.60 | 78.08 | 50.75 | 57.93 | 72.93 | CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI | |
| BERT$_{senet}^{c}$ | 66.02 | 81.99 | 61.59 | 77.71 | 53.4 | 55.42 | 71.89 | CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI | |
| BERT$^{c}$ | 66.32 | 80.98 | 63.35 | 78.08 | 55.29 | 53.90 | 72.69 | CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI |
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