{"id":"W3153390169","doi":"10.1145/3404835.3463049","title":"Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Computer science; Inference; Transformer; Artificial intelligence; Machine learning; Data mining; Engineering","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":[0.001626007,0.0008101171,0.001065457,0.0008816461,0.0003431079,0.001208309,0.00161938,0.000998702,0.002536569],"category_scores_gemma":[0.003388371,0.0003222515,0.0008564338,0.0008549849,0.0004790944,0.003204141,0.0008761207,0.001565058,0.002093973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001479,"about_ca_system_score_gemma":0.00137519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01256323,"about_ca_topic_score_gemma":0.01629481,"domain_scores_codex":[0.9994199,0.000154353,0.00003869338,0.0001679578,0.0001247924,0.00009432422],"domain_scores_gemma":[0.9988293,0.0004220907,0.00009357162,0.0002584101,0.0003336525,0.00006291358],"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":[0.0007004631,0.0004981123,0.003189202,0.000199088,0.0001789723,0.0001412796,0.0001410888,0.5159962,0.01831496,0.0116865,0.01192419,0.4370301],"study_design_scores_gemma":[0.0000115596,0.00004767396,0.0001442928,0.00000251235,0.00001389987,0.00002158567,0.000005746277,0.9960454,0.001577404,0.001741909,0.0003794887,0.000008418278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1689406,0.00161045,0.8131459,0.0006158582,0.0001790219,0.0001297934,0.0005338528,0.008588744,0.006255811],"genre_scores_gemma":[0.8899623,0.0004967281,0.09465974,0.0003104567,0.0001072214,0.00008262009,0.001335129,0.000387566,0.01265825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01256323,"threshold_uncertainty_score":0.02498019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03081914268181116,"score_gpt":0.256862533281866,"score_spread":0.2260433906000549,"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."}}