{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001470594,0.00004348072,0.00003622005,0.0000881998,0.00005861183,0.00005106795,0.0001234171,0.00002408323,0.00002576741],"category_scores_gemma":[0.000003526048,0.00004124669,0.00002306889,0.0003473586,0.000003223789,0.0003325213,0.00002761089,0.00003494864,0.0004250027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002864914,"about_ca_system_score_gemma":0.000007372993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005775113,"about_ca_topic_score_gemma":0.00004930748,"domain_scores_codex":[0.9994537,0.00001914083,0.0001044084,0.0001729601,0.0001450983,0.0001047333],"domain_scores_gemma":[0.999754,0.00001729211,0.00002028738,0.000164086,0.00002070098,0.00002370292],"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":[0.000002655069,0.00001141397,0.000718521,0.00000881107,0.000008792463,0.000004080207,0.0007503362,0.01153925,0.0533732,0.0138206,0.00008797566,0.9196744],"study_design_scores_gemma":[0.0001224056,0.00001641421,0.009935099,0.000002040762,0.000001440067,0.000002292486,0.000007713351,0.9618576,0.01998654,0.007868737,0.0001425067,0.00005719501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06314266,0.000004301651,0.933789,0.0006562368,0.0003625226,0.0000781883,7.778458e-8,0.0005394297,0.001427559],"genre_scores_gemma":[0.9822034,0.000003578106,0.01725817,0.00006807019,0.00004205614,0.000007172077,0.00000225041,0.000003200317,0.000412067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9503184,"threshold_uncertainty_score":0.5462691,"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."}}