{"id":"W3169035567","doi":"10.1145/3447548.3467196","title":"Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Named-entity recognition; Leverage (statistics); Artificial intelligence; Natural language processing; Ranking (information retrieval); Entity linking; Component (thermodynamics); Benchmark (surveying); Labeled data; Machine learning; Information retrieval; Knowledge base","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":[0.002615432,0.001230853,0.00131473,0.001565156,0.001162849,0.001332801,0.003080766,0.001624675,0.005482432],"category_scores_gemma":[0.007524256,0.0006151634,0.001260051,0.001803022,0.001232876,0.004589866,0.004156209,0.003038551,0.003078768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014094,"about_ca_system_score_gemma":0.002352467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009699367,"about_ca_topic_score_gemma":0.01473612,"domain_scores_codex":[0.9978879,0.0005805457,0.0001354741,0.0008039153,0.0004032406,0.0001888862],"domain_scores_gemma":[0.9969417,0.001392911,0.0001606498,0.0008794476,0.0005328938,0.00009232644],"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":[0.0002897863,0.0003264778,0.001436977,0.0002738985,0.000166803,0.0003441625,0.000342046,0.1870702,0.01178489,0.01977868,0.01372204,0.7644641],"study_design_scores_gemma":[0.00002444753,0.00005679713,0.0002651208,0.0000209183,0.00002651862,0.00006866884,0.00005500281,0.9670147,0.008539622,0.01719318,0.006704155,0.00003081229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02243468,0.0008475517,0.9629117,0.0003808245,0.0001662734,0.0001140567,0.0005451619,0.008311502,0.004288335],"genre_scores_gemma":[0.4681301,0.0004329361,0.5150721,0.0007597559,0.0001479672,0.0002831178,0.004992118,0.0008438681,0.009338114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009699367,"threshold_uncertainty_score":0.01928586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0514276670580911,"score_gpt":0.3317543473296186,"score_spread":0.2803266802715275,"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."}}