{"id":"W4393716831","doi":"10.1145/3654660","title":"Computational Politeness in Natural Language Processing: A Survey","year":2024,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Topic Modeling","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Ministry of Electronics and Information technology","keywords":"Computer science; Politeness; Natural language processing; Natural language; Natural (archaeology); Artificial intelligence; Linguistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006846322,0.0006341196,0.001546577,0.0007151969,0.0001174641,0.0007205749,0.00356052,0.0002817449,0.00000238149],"category_scores_gemma":[0.0008355142,0.0005418067,0.0003001931,0.00220831,0.00005046091,0.0002565209,0.00234285,0.00116338,0.0001324188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000287287,"about_ca_system_score_gemma":0.0009984401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009343876,"about_ca_topic_score_gemma":0.0002387939,"domain_scores_codex":[0.993273,0.00294502,0.001137258,0.001349627,0.000581477,0.0007135887],"domain_scores_gemma":[0.9962548,0.001849951,0.0003673868,0.001252324,0.0001638379,0.0001116949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[2.556727e-7,0.00002716248,0.0001330678,0.004731794,0.00003453897,0.0001049871,0.0004489815,0.0005649055,1.037566e-8,0.0003409957,0.0001051052,0.9935082],"study_design_scores_gemma":[0.0003168519,0.0000243223,0.003795685,0.02226751,0.00009929328,0.0002604258,0.0000211186,0.9389303,8.895002e-8,0.001168665,0.03147274,0.001642995],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008402851,0.8728494,0.1248114,0.00004312475,0.001342755,0.0003528146,0.00002383165,0.0004190328,0.00007357067],"genre_scores_gemma":[0.07695957,0.8084586,0.1099688,0.0003053094,0.001375717,0.0000664722,0.001606025,0.0003742541,0.0008852481],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9918652,"threshold_uncertainty_score":0.9997033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08069860879599831,"score_gpt":0.380618364958453,"score_spread":0.2999197561624546,"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."}}