{"id":"W4381714234","doi":"10.48550/arxiv.2306.12245","title":"Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tsinghua Shenzhen International Graduate School; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"End-to-end principle; Labrador Retriever; Computer science; Task (project management); Reading (process); Entity linking; End user; Pipeline (software); Artificial intelligence; Knowledge base; World Wide Web; Engineering; Linguistics; Programming language; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00348237,0.002651724,0.001369908,0.001706597,0.001113853,0.001844288,0.005073539,0.003716053,0.008623184],"category_scores_gemma":[0.008810909,0.0009887627,0.001527967,0.001568167,0.001147397,0.005926769,0.003625332,0.004297482,0.008623901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009242126,"about_ca_system_score_gemma":0.001434171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004460018,"about_ca_topic_score_gemma":0.009589251,"domain_scores_codex":[0.9981998,0.0005467986,0.00009514165,0.0007562905,0.0002227115,0.0001791686],"domain_scores_gemma":[0.9960241,0.00207549,0.0001591875,0.0008823783,0.0007190781,0.0001397583],"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":[0.0006045053,0.0007569623,0.004613437,0.0005382959,0.0002358967,0.000556073,0.0007200214,0.1291781,0.01505147,0.009531518,0.02932812,0.8088856],"study_design_scores_gemma":[0.00004649813,0.0002137415,0.0006718236,0.00004117307,0.00007486758,0.0002227347,0.0001417411,0.9553601,0.01774615,0.01809742,0.007342063,0.00004178915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02315434,0.000922445,0.9566575,0.0005554687,0.0001314981,0.0002799749,0.0007424091,0.01319035,0.004365982],"genre_scores_gemma":[0.3813453,0.0009092268,0.5758882,0.001619204,0.0002256582,0.0008077716,0.009108557,0.001294483,0.02880166],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008623184,"threshold_uncertainty_score":0.0288474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1349473772410979,"score_gpt":0.2216361113038582,"score_spread":0.08668873406276037,"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."}}