{"id":"W3100107515","doi":"10.18653/v1/2020.findings-emnlp.63","title":"Document Ranking with a Pretrained Sequence-to-Sequence Model","year":2020,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":414,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Computer science; Ranking (information retrieval); Sequence (biology); Encoder; Relevance (law); Artificial intelligence; Transformer; Task (project management); Information retrieval; Machine learning; Natural language processing; Data mining; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001633267,0.001034252,0.001177197,0.001172069,0.0004366365,0.00137022,0.001907405,0.001648138,0.006395116],"category_scores_gemma":[0.004292347,0.0005460125,0.0009966518,0.00131691,0.0006395476,0.003314969,0.0006740945,0.002312725,0.004700397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316016,"about_ca_system_score_gemma":0.001346013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005607946,"about_ca_topic_score_gemma":0.01160927,"domain_scores_codex":[0.999314,0.0002198881,0.0000429348,0.0001990787,0.00014369,0.00008055528],"domain_scores_gemma":[0.9982114,0.0009538711,0.0001320284,0.000281724,0.0003517485,0.00006917467],"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":[0.0003507402,0.0003121787,0.001335291,0.0002294301,0.0001054394,0.000151051,0.00009665953,0.6466268,0.00843034,0.01890357,0.01125096,0.3122075],"study_design_scores_gemma":[0.00001330891,0.00007242337,0.0001593582,0.000006927297,0.00001522267,0.00004709929,0.000007775307,0.9898121,0.001243443,0.007769107,0.000842837,0.0000104576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0389448,0.0006051779,0.9490086,0.0007372547,0.0001474855,0.0002364425,0.0007849678,0.004130311,0.005404962],"genre_scores_gemma":[0.7078541,0.0006593933,0.2504365,0.00078615,0.0003527096,0.0006784671,0.002845371,0.000540605,0.03584675],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006395116,"threshold_uncertainty_score":0.02139384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07025758913945321,"score_gpt":0.2760678251045512,"score_spread":0.205810235965098,"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."}}