{"id":"W2900341241","doi":"10.18653/v1/w18-6250","title":"UBC-NLP at IEST 2018: Learning Implicit Emotion With an Ensemble of Language Models","year":2018,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Ranking (information retrieval); Natural language processing; Baseline (sea); Language model; Training set; Ensemble forecasting; Machine learning; Speech recognition","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":[],"consensus_categories":[],"category_scores_codex":[0.0001716753,0.00009017938,0.0001162903,0.00005796281,0.0001019259,0.00004228934,0.00036489,0.00004218103,0.00004168774],"category_scores_gemma":[0.000006786495,0.00007085383,0.00002125372,0.0001341785,0.00003499411,0.0005666685,0.0001861438,0.00007042967,0.00003568684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003218415,"about_ca_system_score_gemma":0.00002779059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005087368,"about_ca_topic_score_gemma":0.0003060261,"domain_scores_codex":[0.9991201,0.00003740411,0.000146196,0.0002962097,0.000197036,0.0002030316],"domain_scores_gemma":[0.9992579,0.00001890684,0.00007233871,0.000502763,0.00008325249,0.0000647996],"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":[0.0001280246,0.0003153016,0.006945979,0.0001182726,0.00008971024,0.00003897756,0.06009454,0.1259109,0.2181084,0.2950825,0.001097311,0.29207],"study_design_scores_gemma":[0.0002127994,0.0004681949,0.0003380488,0.00001806134,0.000003560992,0.00002045192,0.0002719209,0.9782261,0.01891509,0.001294203,0.0001030343,0.0001285536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4600168,0.00000850474,0.5299124,0.00005142975,0.00003745,0.00004049033,1.592389e-7,0.00008964762,0.009843165],"genre_scores_gemma":[0.89829,0.000001344029,0.09897637,0.00007342658,0.0000745045,0.000001746469,0.000001400287,0.000007898911,0.002573273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8523152,"threshold_uncertainty_score":0.2889335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0231619943106445,"score_gpt":0.2526275246139082,"score_spread":0.2294655303032637,"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."}}