{"id":"W3035908455","doi":"10.1007/978-3-030-51310-8_18","title":"Literary Natural Language Generation with Psychological Traits","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Parsing; Creativity; Computer science; Artificial intelligence; Natural language processing; Text generation; Natural language generation; Natural (archaeology); Personality; Literary language; Linguistics; Natural language; Psychology; History; Philosophy; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007198293,0.0002846376,0.0001717635,0.0006852735,0.0005160251,0.002132993,0.0005707026,0.0004654563,0.01110495],"category_scores_gemma":[0.007119375,0.0002994804,0.0003308185,0.000539982,0.001074936,0.002518719,0.001147246,0.0008147427,0.001747247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004283438,"about_ca_system_score_gemma":0.0002302809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002285145,"about_ca_topic_score_gemma":0.0002655876,"domain_scores_codex":[0.9993642,0.0003647088,0.00002400476,0.0001035535,0.0001128302,0.00003083748],"domain_scores_gemma":[0.9970171,0.002099158,0.0001189358,0.0003708133,0.0002973652,0.00009651796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001522132,0.000180123,0.00485402,0.0002217752,0.00003797105,0.0008053654,0.004822079,0.007474395,0.007651143,0.7763009,0.01204483,0.1854552],"study_design_scores_gemma":[0.00004773952,0.0001322882,0.00500304,0.00009296185,0.00003842166,0.001191103,0.001691596,0.1203721,0.007302402,0.8316217,0.03246444,0.00004227785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2608498,0.0007090261,0.4311884,0.002475507,0.0003412749,0.0001650499,0.0004867667,0.002201891,0.3015822],"genre_scores_gemma":[0.9353905,0.0001464593,0.03732661,0.0000982287,0.00008744977,0.00007090237,0.0004407896,0.0003437092,0.02609532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01110495,"threshold_uncertainty_score":0.03714973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03215910308621596,"score_gpt":0.2888146522133143,"score_spread":0.2566555491270983,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}