{"id":"W4387854289","doi":"10.1145/3583780.3615205","title":"Latent Aspect Detection via Backtranslation Augmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Context (archaeology); Laptop; Vocabulary; SemEval; Natural language processing; Benchmark (surveying); Focus (optics); Artificial intelligence; Data science; Semantics (computer science); Latent semantic analysis; Natural language; World Wide Web; Task (project management); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001883294,0.001805209,0.001130083,0.003337159,0.000599812,0.001722006,0.001055987,0.0008446959,0.002049653],"category_scores_gemma":[0.01343238,0.0004871738,0.001829431,0.00270591,0.0008687882,0.003131518,0.00207831,0.001776833,0.003569425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005589328,"about_ca_system_score_gemma":0.001311903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451141,"about_ca_topic_score_gemma":0.006007697,"domain_scores_codex":[0.9967361,0.001135792,0.0003634719,0.0008720153,0.0007413314,0.0001513075],"domain_scores_gemma":[0.9905214,0.00381598,0.001045791,0.002195231,0.002263811,0.0001577062],"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":[0.0009487417,0.0004191643,0.0158056,0.001533679,0.0003267573,0.0009002118,0.002000423,0.00979429,0.1043351,0.005211214,0.04759407,0.8111309],"study_design_scores_gemma":[0.0002532778,0.0007150074,0.01839283,0.0002279039,0.000542426,0.003478844,0.001295308,0.6927775,0.1295898,0.03057688,0.1218659,0.0002843033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1600628,0.006505678,0.7832699,0.001642167,0.001206658,0.0008258186,0.01052497,0.02901652,0.006945372],"genre_scores_gemma":[0.4920774,0.001650867,0.4678139,0.001136969,0.00101883,0.0008195752,0.0277507,0.001669368,0.006062284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003337159,"threshold_uncertainty_score":0.009959936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03564317263585555,"score_gpt":0.2604512646798721,"score_spread":0.2248080920440166,"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."}}