{"id":"W2996912688","doi":"","title":"Discreteness in Neural Natural Language Processing","year":2019,"lang":"en","type":"article","venue":"Empirical Methods in Natural Language Processing","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Focus (optics); Process (computing); Point (geometry); Space (punctuation); Natural language processing; Natural language; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002696853,0.0006411536,0.0008731439,0.001451712,0.0005170694,0.003094788,0.001273792,0.001437101,0.005647586],"category_scores_gemma":[0.008808486,0.00055805,0.0008259129,0.002033613,0.003438078,0.007027759,0.00167771,0.004527737,0.001170104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001709879,"about_ca_system_score_gemma":0.0009223366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001710346,"about_ca_topic_score_gemma":0.001486802,"domain_scores_codex":[0.9986045,0.0005509247,0.0001267985,0.0002790199,0.0003788133,0.00006007013],"domain_scores_gemma":[0.9967226,0.002725398,0.0001036021,0.0002288769,0.0001648482,0.00005470367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008682474,0.00001257172,0.0001717305,0.0001868056,0.00001803061,0.00004403601,0.0001196462,0.008050798,0.0002956744,0.9580468,0.00289331,0.03015183],"study_design_scores_gemma":[0.000002290688,0.000006364479,0.0001062196,0.00006262793,0.000003687678,0.00004674579,0.00001719637,0.0318422,0.0001793849,0.9566795,0.0110448,0.000008947861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003614689,0.02525586,0.943899,0.004830928,0.0004760487,0.00004962716,0.0003468149,0.0002985745,0.02122846],"genre_scores_gemma":[0.3193847,0.06627134,0.5783498,0.003323009,0.003965212,0.0007999758,0.001453182,0.0004947727,0.025958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005647586,"threshold_uncertainty_score":0.01889312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03199366781166351,"score_gpt":0.4167666648137828,"score_spread":0.3847729970021193,"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."}}