{"id":"W3033346395","doi":"10.18653/v1/2020.ngt-1.20","title":"Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Paraphrase; Computer science; Fluency; Macro; Machine translation; Artificial intelligence; Task (project management); Natural language processing; Language model; Translation (biology); Portuguese; Programming language; Linguistics","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":[0.002867962,0.001413673,0.001198482,0.0008430463,0.000762332,0.00161136,0.002387868,0.002207463,0.002997279],"category_scores_gemma":[0.009908143,0.0009809233,0.001271397,0.0011084,0.001061374,0.002429442,0.002024241,0.002666062,0.001456455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167945,"about_ca_system_score_gemma":0.001139971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01374076,"about_ca_topic_score_gemma":0.01927578,"domain_scores_codex":[0.9987711,0.0005868932,0.00003929259,0.0004084531,0.000102086,0.0000922185],"domain_scores_gemma":[0.9969517,0.002004619,0.000143358,0.0004643651,0.0002857307,0.0001502301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003250084,0.0001302193,0.001829133,0.00007765395,0.0001259467,0.0001197419,0.0003070737,0.8988906,0.001296913,0.003250912,0.004907701,0.08873912],"study_design_scores_gemma":[0.00001499596,0.00003988628,0.0001988476,0.00000571998,0.00001105361,0.00001350357,0.00002006588,0.9930685,0.0005136903,0.005519757,0.0005873262,0.000006687761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1925799,0.001580227,0.7908281,0.001623188,0.0002685813,0.0002498706,0.00141783,0.006542949,0.004909361],"genre_scores_gemma":[0.799979,0.0003230245,0.1877498,0.0005375887,0.0001897272,0.0004332484,0.002744531,0.0007653822,0.007277832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01374076,"threshold_uncertainty_score":0.02732158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04705299505658658,"score_gpt":0.3118409787660523,"score_spread":0.2647879837094658,"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."}}