{"id":"W2963506530","doi":"10.18653/v1/p19-1602","title":"Generating Sentences from Disentangled Syntactic and Semantic Spaces","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Paraphrase; Natural language processing; Artificial intelligence; Syntax; Language model; Latent semantic analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007793315,0.0009610806,0.0006598045,0.0006153581,0.0003200562,0.0005540177,0.0008844803,0.0008965608,0.002356681],"category_scores_gemma":[0.003402595,0.0004124191,0.001271792,0.0005772053,0.0004582509,0.001438087,0.001088504,0.001295586,0.000850795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003727173,"about_ca_system_score_gemma":0.0008908703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009826968,"about_ca_topic_score_gemma":0.002150001,"domain_scores_codex":[0.999402,0.0002664116,0.00003378862,0.0001642133,0.00009249579,0.00004109054],"domain_scores_gemma":[0.9985046,0.001022638,0.00008269166,0.000154236,0.0001853648,0.00005042178],"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":[0.000443608,0.0003417003,0.002597574,0.0007792456,0.0002506101,0.001005395,0.0008567821,0.2790004,0.08244657,0.08013692,0.01687137,0.5352699],"study_design_scores_gemma":[0.0000330782,0.00005074746,0.0002622849,0.00001468752,0.00003370167,0.0001065399,0.00004022315,0.9623027,0.01023394,0.02506603,0.001841216,0.00001493076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04422743,0.0003640893,0.9508805,0.0003410266,0.0001009438,0.0001360352,0.0005281409,0.001597644,0.001824177],"genre_scores_gemma":[0.6223118,0.0004296664,0.3675518,0.0002792355,0.0001581581,0.0004609456,0.003283522,0.0005241012,0.005000782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002356681,"threshold_uncertainty_score":0.007883847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02709185134092034,"score_gpt":0.2542554377898595,"score_spread":0.2271635864489392,"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."}}