{"url":"/sota/personality-recognition-in-conversation-on-1","task":{"name":"Personality Recognition in Conversation","url":"/task/personality-recognition-in-conversation","note":null},"dataset":{"name":"CPED","url":"/dataset/cped"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"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:\r\n\r\n$Per_i = argmax_{Per'_i}P(Per'_i | C_{i,j}, \\cdots, C_{i,N})$\r\n\r\nwhere $Per_i=[Neu, Ext, Ope, Agr, Con]$ is a 5-dimensional vector representing Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. $C_{i,j}$ is the conversations between $Speaker_i$ and $Speaker_j$ ($1 \\leq j \\leq N$).","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy (%)","Macro-F1","Accuracy of Neurotism","Accuracy of Extraversion","Accuracy of Openness","Accuracy of Agreeableness","Accuracy of Conscientiousness"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy (%)":"higher","Macro-F1":"higher","Accuracy of Neurotism":"higher","Accuracy of Extraversion":"higher","Accuracy of Openness":"higher","Accuracy of Agreeableness":"higher","Accuracy of Conscientiousness":"higher"}},"counts":{"rows":4,"rows_with_code":4,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"BERT$_{ssenet}^{c}$","metrics":{"Accuracy (%)":"67.25","Accuracy of Agreeableness":"85.89","Accuracy of Conscientiousness":"63.48","Accuracy of Extraversion":"78.21","Accuracy of Neurotism":"53.27","Accuracy of Openness":"55.42","Macro-F1":"74.08"},"uses_additional_data":false,"paper_date":"2022-05-29","paper":"/paper/cped-a-large-scale-chinese-personalized-and-1","paper_url":"https://arxiv.org/abs/2205.14727v1","paper_title":"CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI","code":"https://github.com/scutcyr/CPED","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"BERT$^{s}$","metrics":{"Accuracy (%)":"67.23","Accuracy of Agreeableness":"85.76","Accuracy of Conscientiousness":"63.60","Accuracy of Extraversion":"78.08","Accuracy of Neurotism":"50.75","Accuracy of Openness":"57.93","Macro-F1":"72.93"},"uses_additional_data":false,"paper_date":"2022-05-29","paper":"/paper/cped-a-large-scale-chinese-personalized-and-1","paper_url":"https://arxiv.org/abs/2205.14727v1","paper_title":"CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI","code":"https://github.com/scutcyr/CPED","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"BERT$^{c}$","metrics":{"Accuracy (%)":"66.32","Accuracy of Agreeableness":"80.98","Accuracy of Conscientiousness":"63.35","Accuracy of Extraversion":"78.08","Accuracy of Neurotism":"55.29","Accuracy of Openness":"53.90","Macro-F1":"72.69"},"uses_additional_data":false,"paper_date":"2022-05-29","paper":"/paper/cped-a-large-scale-chinese-personalized-and-1","paper_url":"https://arxiv.org/abs/2205.14727v1","paper_title":"CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI","code":"https://github.com/scutcyr/CPED","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"BERT$_{senet}^{c}$","metrics":{"Accuracy (%)":"66.02","Accuracy of Agreeableness":"81.99","Accuracy of Conscientiousness":"61.59","Accuracy of Extraversion":"77.71","Accuracy of Neurotism":"53.4","Accuracy of Openness":"55.42","Macro-F1":"71.89"},"uses_additional_data":false,"paper_date":"2022-05-29","paper":"/paper/cped-a-large-scale-chinese-personalized-and-1","paper_url":"https://arxiv.org/abs/2205.14727v1","paper_title":"CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI","code":"https://github.com/scutcyr/CPED","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 7,081 of the 9,623 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9623,"papers_checked":7081,"papers_extracted_not_yet_verified":217,"boards_without_verdict":27,"papers_not_yet_extracted":2325},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-25T09:33:49+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}