{"id":"W2399880602","doi":"10.1609/aaai.v31i1.10983","title":"A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues","year":2017,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":266,"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; Context (archaeology); Variable (mathematics); Artificial neural network; Process (computing); Encoder; Machine learning; Task (project management); Mathematics; Engineering","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":[0.001826564,0.000785258,0.0006308528,0.0006664997,0.0004003156,0.0008479111,0.00156732,0.001351961,0.004114923],"category_scores_gemma":[0.005816707,0.0006030776,0.0008672085,0.000783503,0.0006976786,0.001462466,0.00104987,0.002071222,0.001402666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009869706,"about_ca_system_score_gemma":0.001193766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004889777,"about_ca_topic_score_gemma":0.007904901,"domain_scores_codex":[0.9991054,0.0004548704,0.00003956445,0.00021829,0.0001127911,0.00006908795],"domain_scores_gemma":[0.997772,0.001676795,0.0001154056,0.000146173,0.0002117753,0.00007781691],"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":[0.000594786,0.0001941489,0.002140855,0.0002640819,0.0001391064,0.0003158968,0.0008445431,0.7428105,0.01004121,0.08645752,0.005839127,0.1503582],"study_design_scores_gemma":[0.00001445125,0.00001772841,0.00007847111,0.000005349767,0.000009764588,0.00002335599,0.000008004518,0.9899182,0.0004981742,0.008949878,0.0004702242,0.00000641198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01573862,0.0002706356,0.9802473,0.0004614954,0.00006503199,0.00007346088,0.0003497094,0.001170079,0.001623702],"genre_scores_gemma":[0.625735,0.0003710089,0.3616284,0.0003208843,0.0001409859,0.0005460798,0.001253875,0.0003475849,0.009656114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004889777,"threshold_uncertainty_score":0.01376581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2165077802991398,"score_gpt":0.3321390255953345,"score_spread":0.1156312452961947,"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."}}