{"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,"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."}}