{"id":"W2974191861","doi":"10.18653/v1/k19-1008","title":"Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Focus (optics); Linguistics; Task (project management); Natural language processing; Second language; Artificial intelligence; Psychology; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001389868,0.0005352107,0.0005160961,0.0009772507,0.0007368343,0.002217888,0.000861375,0.0009910341,0.003873418],"category_scores_gemma":[0.005888029,0.0003155365,0.0002324467,0.0007077533,0.0007312417,0.002090344,0.001211487,0.0009962489,0.002536364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003076885,"about_ca_system_score_gemma":0.0004621992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001760558,"about_ca_topic_score_gemma":0.002030127,"domain_scores_codex":[0.9989748,0.0002747721,0.00006649276,0.0003713515,0.0002200167,0.00009257164],"domain_scores_gemma":[0.9963222,0.002050438,0.0002733897,0.0005408327,0.000641973,0.0001712221],"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":[0.001280993,0.0003679471,0.04623498,0.0004248641,0.00008857484,0.001694633,0.004627495,0.005976781,0.1887165,0.01929877,0.01815067,0.7131376],"study_design_scores_gemma":[0.0001135254,0.0003184121,0.04733774,0.00009343755,0.00009950408,0.002912401,0.004080361,0.5699139,0.2847635,0.07025088,0.01994371,0.0001726117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7623183,0.0004397632,0.2172022,0.001106687,0.0001841166,0.00007317828,0.0008669966,0.008662887,0.009145847],"genre_scores_gemma":[0.9282603,0.00008520338,0.06635925,0.0001016446,0.00004411593,0.00001735592,0.0007477704,0.0004093657,0.003974914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003873418,"threshold_uncertainty_score":0.01295781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01258778493028795,"score_gpt":0.2581733714810592,"score_spread":0.2455855865507713,"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."}}