{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004061476,0.0009523025,0.0005256803,0.001545993,0.0005556867,0.001261248,0.0007883268,0.001089241,0.001708784],"category_scores_gemma":[0.02952434,0.0002221904,0.0003666626,0.0008255651,0.0006259797,0.002492539,0.001627071,0.001222733,0.001930219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004402137,"about_ca_system_score_gemma":0.000834483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002405274,"about_ca_topic_score_gemma":0.004293432,"domain_scores_codex":[0.9953461,0.001538673,0.0004816427,0.0009508895,0.001437225,0.0002454591],"domain_scores_gemma":[0.9736785,0.01278765,0.003880508,0.003193852,0.005844962,0.0006144898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001027173,0.0006546037,0.4982485,0.0005621655,0.0002651572,0.003320662,0.004597615,0.009687923,0.06001718,0.001364579,0.00820464,0.4120498],"study_design_scores_gemma":[0.00007143235,0.001384716,0.2845087,0.000372243,0.0003888381,0.009392139,0.007794847,0.4238949,0.2414895,0.0109949,0.01939639,0.0003114558],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9341479,0.000342242,0.05631784,0.0004139442,0.000160935,0.0001007327,0.001188087,0.002351178,0.004977035],"genre_scores_gemma":[0.9865129,0.0001026817,0.009976228,0.0001014667,0.00001684359,0.0000355713,0.001094455,0.0001214008,0.002038468],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004061476,"threshold_uncertainty_score":0.02147937,"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."}}