{"url":"/task/personality-recognition-in-conversation","name":"Personality Recognition in Conversation","slug":"personality-recognition-in-conversation","description_markdown":"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 $Speaker_j$ ($1 \\leq j \\leq N$).","categories":[{"name":"Natural Language Processing","url":"/area/natural-language-processing"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":3,"papers_with_code":2,"benchmarks":1,"benchmark_tables_in_archive":1,"benchmark_tables_shown":1,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":1,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/personality-recognition-in-conversation-on-1","slug":"personality-recognition-in-conversation-on-1","dataset":"CPED","dataset_url":"/dataset/cped","rows_in_archive":4,"metrics":["Accuracy (%)","Macro-F1","Accuracy of Neurotism","Accuracy of Extraversion","Accuracy of Openness","Accuracy of Agreeableness","Accuracy of Conscientiousness"],"first_row_in_archive_order":{"model":"BERT$_{ssenet}^{c}$","paper_title":"CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI","paper_url":"/paper/cped-a-large-scale-chinese-personalized-and-1","paper_date":"2022-05-29","arxiv_id":"2205.14727","code_links":[{"title":"scutcyr/CPED","url":"https://github.com/scutcyr/CPED"}],"syntology":null}}],"datasets":[{"url":"/dataset/cped","name":"CPED","full_name":"Chinese Personalized and Emotional Dialogue","num_papers_in_archive":15}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":2,"of":2,"tagged_in_all":3,"items":[{"url":"/paper/affective-nli-towards-accurate-and","title":"Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation","date":"2024-04-03","arxiv_id":"2404.02589","repositories_listed":1,"syntology":null},{"url":"/paper/cped-a-large-scale-chinese-personalized-and-1","title":"CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI","date":"2022-05-29","arxiv_id":"2205.14727","repositories_listed":1,"syntology":null}],"syntology_records":0,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}