{"id":"W4283720480","doi":"10.1007/s10115-022-01703-7","title":"BertHANK: hierarchical attention networks with enhanced knowledge and pre-trained model for answer selection","year":2022,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Selection (genetic algorithm); Computer science; Artificial intelligence; Machine learning; Model selection","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":[0.001612743,0.001578013,0.00117728,0.001343123,0.0008725575,0.001305048,0.003135193,0.003098471,0.01343431],"category_scores_gemma":[0.006169416,0.00102939,0.001042193,0.001367266,0.0005515437,0.004161507,0.002964408,0.003082603,0.005214246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001490288,"about_ca_system_score_gemma":0.001569881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02551643,"about_ca_topic_score_gemma":0.03904148,"domain_scores_codex":[0.9992108,0.000217098,0.0000310509,0.0002663312,0.0001558046,0.0001188309],"domain_scores_gemma":[0.9985726,0.0008419628,0.00004717335,0.0002039564,0.0002493178,0.00008511741],"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":[0.0010752,0.0003655997,0.00093218,0.0003506512,0.0002677906,0.0002190921,0.0002586847,0.09616026,0.0131795,0.01322749,0.07606995,0.7978936],"study_design_scores_gemma":[0.00006224343,0.00003503421,0.0002223233,0.00001347785,0.00003902813,0.00002482841,0.00001897286,0.9798864,0.004019004,0.01290423,0.002755492,0.00001902945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0213907,0.001706913,0.9285182,0.001027676,0.0004555246,0.0002676115,0.002374984,0.03904334,0.005214991],"genre_scores_gemma":[0.3567086,0.0007468638,0.6031391,0.0009444948,0.0003214201,0.0006452149,0.007214138,0.002444282,0.02783581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02551643,"threshold_uncertainty_score":0.05073577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01161435937029967,"score_gpt":0.2366141620126121,"score_spread":0.2249998026423124,"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."}}