{"id":"W4293083978","doi":"10.1145/3533020","title":"Learning Implicit and Explicit Multi-task Interactions for Information Extraction","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China","keywords":"Computer science; Multi-task learning; Generalization; Leverage (statistics); Artificial intelligence; Task (project management); Machine learning; Sequence learning; Unobservable; Representation (politics)","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":[0.0004954591,0.0001514516,0.0001537112,0.0005648671,0.001242755,0.0004907462,0.0004153745,0.00005109738,0.00001236781],"category_scores_gemma":[0.00005824268,0.0001681319,0.00007716275,0.0003580416,0.000008974514,0.007651147,0.00003630291,0.0003878319,0.00005071312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002348621,"about_ca_system_score_gemma":0.00005212467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000151533,"about_ca_topic_score_gemma":0.000004147361,"domain_scores_codex":[0.9985353,0.0000822305,0.0006547351,0.0001647623,0.0003407743,0.0002222411],"domain_scores_gemma":[0.9987084,0.0002207894,0.0003383492,0.0004789262,0.0001752473,0.00007834991],"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":[0.00005044314,0.0000681372,0.00005676291,0.0001382752,0.00005691019,2.593326e-7,0.01429261,0.6421733,0.0004056162,0.01378361,0.0002131501,0.3287609],"study_design_scores_gemma":[0.0008212344,0.0001682832,0.0001537583,0.00001676344,0.00001225809,0.000106862,0.006651078,0.8010163,0.0001657765,0.00008754563,0.1905871,0.0002130356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003940925,0.00001383312,0.9925569,0.0004395607,0.00144946,0.0007989911,0.00005064423,0.000343407,0.000406257],"genre_scores_gemma":[0.979701,0.00001211297,0.01830113,0.000280076,0.00003251629,0.001384314,0.00006788238,0.00000773391,0.0002131857],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9757601,"threshold_uncertainty_score":0.9558395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02740170172849626,"score_gpt":0.2773413987875704,"score_spread":0.2499396970590741,"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."}}