{"id":"W3015071427","doi":"10.48550/arxiv.2004.01940","title":"Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Adaptation (eye); Computer science; Task (project management); Track (disk drive); Artificial neural network; Artificial intelligence; Deep neural networks; Natural language processing; Human–computer interaction; Engineering; Psychology; Systems engineering","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":[0.002083863,0.001596516,0.0007738947,0.0008163646,0.0006275686,0.001154164,0.001726376,0.001348797,0.002239526],"category_scores_gemma":[0.004911149,0.0006638406,0.0009384817,0.000472609,0.0004605583,0.002629979,0.001714121,0.002937889,0.001233046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0012347,"about_ca_system_score_gemma":0.0009997354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01076649,"about_ca_topic_score_gemma":0.0165222,"domain_scores_codex":[0.9989738,0.0003805043,0.00004795615,0.000385961,0.00009332359,0.0001184066],"domain_scores_gemma":[0.9983994,0.000944872,0.00007123427,0.0001510867,0.0003275917,0.0001057301],"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":[0.0005709634,0.0005308506,0.001547777,0.0003208577,0.0002481445,0.0001529623,0.000651308,0.513113,0.02090069,0.003180924,0.007633788,0.4511487],"study_design_scores_gemma":[0.0000107288,0.0000590797,0.0002323512,0.00001140468,0.00002270618,0.00001094614,0.00003783788,0.9946268,0.002388398,0.001832612,0.0007576408,0.000009443569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1601655,0.003477607,0.8146067,0.0008534012,0.0004944456,0.0003324766,0.0005031861,0.01130276,0.008263969],"genre_scores_gemma":[0.8154672,0.0006781695,0.1721092,0.000374395,0.0002292425,0.0003474526,0.001517384,0.0004361525,0.008840848],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01076649,"threshold_uncertainty_score":0.02140766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05558461328526256,"score_gpt":0.1960720369978679,"score_spread":0.1404874237126054,"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."}}