{"id":"W3022187094","doi":"10.1609/aaai.v31i1.10983","title":"A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues","year":2017,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":702,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal; McGill University; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Samsung; Compute Canada; Samsung Advanced Institute of Technology; Canadian Institute for Advanced Research","keywords":"Computer science; Latent variable; Generative grammar; Generative model; Latent variable model; Artificial intelligence; Variable (mathematics); Artificial neural network; Encoder; Task (project management); Machine learning; Mathematics","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":[0.001655264,0.0007402865,0.0005835815,0.0006155845,0.0003768189,0.000722288,0.001455978,0.001201599,0.003662721],"category_scores_gemma":[0.005099402,0.0005496513,0.000800672,0.000713076,0.0006123235,0.001315971,0.0009337185,0.001921377,0.001228312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009188981,"about_ca_system_score_gemma":0.001137751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005200295,"about_ca_topic_score_gemma":0.008891263,"domain_scores_codex":[0.9992504,0.0003807216,0.00003457821,0.0001819077,0.00009407,0.00005836106],"domain_scores_gemma":[0.9980733,0.001463153,0.00009773751,0.0001174688,0.0001821834,0.00006614991],"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":[0.0005125357,0.0001837368,0.001935252,0.0002458591,0.0001312384,0.0002708102,0.0007241999,0.7618315,0.01051327,0.06516249,0.005012923,0.1534762],"study_design_scores_gemma":[0.00001203923,0.0000165757,0.00007630081,0.000004822493,0.000009237792,0.00001988621,0.000007029505,0.9926433,0.0005092242,0.006288574,0.0004072414,0.00000578479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0157297,0.0002584345,0.9806574,0.0003901287,0.0000564687,0.00006978428,0.00031398,0.001135359,0.00138869],"genre_scores_gemma":[0.6180487,0.0003620579,0.3707715,0.0002779547,0.0001221277,0.0005419916,0.001178263,0.0003103024,0.00838696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005200295,"threshold_uncertainty_score":0.01225305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09665800747503062,"score_gpt":0.2927783749652687,"score_spread":0.1961203674902381,"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."}}