{"id":"W4225580830","doi":"10.18653/v1/2022.acl-long.396","title":"Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","topic":"Topic Modeling","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"National Natural Science Foundation of China; Tencent","keywords":"Computer science; Semantic reasoner; Information retrieval; Embedding; Question answering; Knowledge base; Heuristic; Process (computing); Pruning; Base (topology); Artificial intelligence; Programming language; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002528907,0.0001989743,0.0002995088,0.000127819,0.0009029069,0.0000745209,0.001357483,0.00008738593,9.185985e-7],"category_scores_gemma":[0.01244757,0.0001803409,0.0003546691,0.0005404813,0.00004981353,0.0001195809,0.0006611322,0.0002599119,3.865833e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004595349,"about_ca_system_score_gemma":0.000245038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001494971,"about_ca_topic_score_gemma":0.00000580718,"domain_scores_codex":[0.9976323,0.00004341139,0.0007078873,0.000435304,0.0008081939,0.000372946],"domain_scores_gemma":[0.9941543,0.0006199357,0.00149211,0.0001933949,0.003477238,0.00006306904],"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":[0.0001090349,0.000210049,0.009853798,0.0003144275,0.0001094775,4.367496e-8,0.005060886,0.9106408,0.003931835,0.06880424,0.0005825976,0.0003828003],"study_design_scores_gemma":[0.0007770283,0.00007395903,0.00172717,0.0001000929,0.00006756405,7.230943e-7,0.0002349754,0.9824486,0.003087499,0.0108642,0.0004202407,0.0001979149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2344202,0.000222922,0.7525522,0.001542383,0.005347023,0.002882758,0.0007439071,0.0003037579,0.001984906],"genre_scores_gemma":[0.887943,0.000001201633,0.110471,0.00009666212,0.0002229402,0.00006021559,0.00001120795,0.00002624729,0.001167434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6535228,"threshold_uncertainty_score":0.995871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01924940758376219,"score_gpt":0.2685602883043876,"score_spread":0.2493108807206254,"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."}}