{"id":"W4221016102","doi":"10.1007/s00521-022-07072-0","title":"Mutually improved dense retriever and GNN-based reader for arbitrary-hop open-domain question answering","year":2022,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Labrador Retriever; Question answering; Open domain; Asynchronous communication; Leverage (statistics); Graph; Information retrieval; Preprocessor; Artificial intelligence; Theoretical computer science; Computer network","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":[0.001558109,0.001519378,0.002667268,0.002189587,0.0009712452,0.002152384,0.003361476,0.002649359,0.01457309],"category_scores_gemma":[0.007186946,0.0006275797,0.001495487,0.001814585,0.0009799453,0.006050974,0.004544927,0.00216332,0.01145761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007594115,"about_ca_system_score_gemma":0.002022084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004713333,"about_ca_topic_score_gemma":0.01123137,"domain_scores_codex":[0.9977016,0.0004644805,0.0001613789,0.0007397859,0.0006469505,0.0002858109],"domain_scores_gemma":[0.996869,0.001000911,0.0001018357,0.001291721,0.0005943106,0.0001422444],"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":[0.001135877,0.0006369547,0.001619713,0.0006909391,0.0002326039,0.0004988037,0.0004369942,0.0273107,0.03001795,0.02832698,0.0520803,0.8570122],"study_design_scores_gemma":[0.000200665,0.0002703118,0.0008687958,0.00005862501,0.00022885,0.0008061278,0.0002989747,0.8783311,0.02429328,0.07383833,0.02070073,0.0001042387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03147912,0.002193198,0.9281663,0.0006970187,0.0004965766,0.0002876425,0.002094486,0.02545752,0.00912806],"genre_scores_gemma":[0.3457319,0.0007952334,0.6151702,0.001186659,0.0006260509,0.0003155052,0.0112839,0.00160103,0.02328951],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01457309,"threshold_uncertainty_score":0.04875189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02400630599384165,"score_gpt":0.2851281038151139,"score_spread":0.2611217978212723,"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."}}