{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000231059,0.0003196493,0.0003932729,0.0001659403,0.0001999311,0.0002118179,0.00102772,0.0002571235,0.000001133638],"category_scores_gemma":[0.00006059702,0.0003780675,0.0001925426,0.0003461938,0.00006883351,0.0002157103,0.0009478804,0.0004142862,0.000001465998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000129108,"about_ca_system_score_gemma":0.0001054221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001620474,"about_ca_topic_score_gemma":0.00001484696,"domain_scores_codex":[0.9977631,0.0001376518,0.0002765325,0.00133044,0.00009372373,0.000398567],"domain_scores_gemma":[0.99833,0.0002378796,0.0002732109,0.0007871968,0.0001488253,0.0002228725],"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":[0.00002939152,0.00002119138,0.001816008,0.000202906,0.00004729959,0.00005494623,0.00007769983,0.9362873,0.00006078245,0.06113714,0.00003845862,0.0002268926],"study_design_scores_gemma":[0.0007552554,0.00005752034,0.0007625282,0.0001314042,0.000080291,0.00000249821,0.00002084122,0.9953751,0.000004650086,0.002370381,0.00008322384,0.0003563221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1822695,0.00008687081,0.8151584,0.0002037465,0.001243299,0.0007217075,0.00001931591,0.0002707428,0.0000264557],"genre_scores_gemma":[0.9930348,0.00001222926,0.006443275,0.0001128669,0.000227281,0.000007523846,0.00004448511,0.00002255601,0.0000949495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8107653,"threshold_uncertainty_score":0.9998671,"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."}}