{"id":"W2304545146","doi":"10.18653/v1/p16-1056","title":"Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Computer science; Natural language processing; Artificial intelligence; Question answering; Sentence; Similarity (geometry); Machine translation; Knowledge base; Baseline (sea); Architecture; Artificial neural network; Information retrieval","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00168255,0.00133309,0.0008562421,0.001620522,0.001292833,0.001383953,0.001616959,0.002374356,0.01181944],"category_scores_gemma":[0.0134358,0.0005159985,0.0007973953,0.001274689,0.0008135154,0.003050714,0.002502253,0.001998466,0.006595902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001198235,"about_ca_system_score_gemma":0.001226696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008836431,"about_ca_topic_score_gemma":0.01432342,"domain_scores_codex":[0.9982311,0.000865616,0.00009689498,0.0004843276,0.000239776,0.00008228234],"domain_scores_gemma":[0.994624,0.003666873,0.0001491553,0.0006941855,0.0006668749,0.0001989104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002246863,0.00121277,0.01141793,0.003841408,0.0003652044,0.002896419,0.005277475,0.02179639,0.02290346,0.01996314,0.4750822,0.4329967],"study_design_scores_gemma":[0.001046241,0.0007460922,0.03134444,0.0006877183,0.0002930553,0.002663579,0.005391411,0.4161917,0.03513747,0.04716838,0.4590549,0.0002750484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6168216,0.009988075,0.1247178,0.007814707,0.002142315,0.001294685,0.188682,0.01844493,0.03009387],"genre_scores_gemma":[0.5213131,0.000866276,0.1095114,0.0006299651,0.0003112577,0.0008849702,0.3529457,0.0009521234,0.01258518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01181944,"threshold_uncertainty_score":0.03953999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02735015464353461,"score_gpt":0.2677936035776921,"score_spread":0.2404434489341574,"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."}}