{"id":"W3154002721","doi":"","title":"Identifying negative language transfer in learner errors using POS information.","year":2021,"lang":"en","type":"article","venue":"Workshop on Innovative Use of NLP for Building Educational Applications","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Mistake; Negative transfer; Language model; Natural language processing; Language transfer; Artificial intelligence; First language; Cache language model; Transfer (computing); Recurrent neural network; Artificial neural network; Speech recognition; n-gram; Natural language; Universal Networking Language; Linguistics; Comprehension approach","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":[0.0002480283,0.0001664873,0.0001946564,0.0005624338,0.0001778325,0.0001848814,0.00047084,0.00009452169,0.00001745252],"category_scores_gemma":[0.0005840244,0.0001742108,0.00006256966,0.003309132,0.00007694206,0.001565831,0.00009447041,0.000273842,0.000003757361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000208248,"about_ca_system_score_gemma":0.0004628435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004000677,"about_ca_topic_score_gemma":0.00001223088,"domain_scores_codex":[0.9986151,0.00004406791,0.0005165434,0.0003350787,0.000268759,0.0002203853],"domain_scores_gemma":[0.9974715,0.0007552361,0.0001964623,0.0003994234,0.001137478,0.00003993513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001157074,0.0001934552,0.000224552,0.00006854953,0.00002718181,4.341621e-7,0.003372345,0.00063411,0.0179528,0.9605986,0.0003630588,0.01655335],"study_design_scores_gemma":[0.001951407,0.00009713349,0.009618852,0.002088006,0.0000546365,0.00005726097,0.005683741,0.02082231,0.5293553,0.4149139,0.01355002,0.001807459],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06051038,0.0001655168,0.9360648,0.002310961,0.0001007834,0.0006147777,0.00004635948,0.00008735776,0.00009906096],"genre_scores_gemma":[0.3868924,0.000004818862,0.6120399,0.0004587256,0.00004255987,0.0003829221,0.00007756793,0.00001122805,0.00008985418],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5456848,"threshold_uncertainty_score":0.7104111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04961372036844343,"score_gpt":0.3623847222147725,"score_spread":0.3127710018463291,"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."}}