{"id":"W2962883855","doi":"10.1609/aaai.v30i1.9883","title":"Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models","year":2016,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1725,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Generative grammar; Computer science; Bootstrapping (finance); Word (group theory); Artificial intelligence; End-to-end principle; Artificial neural network; Language model; Domain (mathematical analysis); Encoder; Recurrent neural network; Task (project management); Natural language processing; Generative model; Linguistics","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":[0.001737216,0.0009312491,0.0008740866,0.0004200005,0.0005862525,0.001221811,0.001634221,0.001399191,0.002955623],"category_scores_gemma":[0.006055079,0.000776016,0.00103853,0.0003194733,0.0007866192,0.002655101,0.002053864,0.002058663,0.002163582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007809123,"about_ca_system_score_gemma":0.0008228096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003029289,"about_ca_topic_score_gemma":0.005048819,"domain_scores_codex":[0.998995,0.0004078019,0.00004285455,0.0003666259,0.0001151818,0.00007258062],"domain_scores_gemma":[0.9977677,0.001519832,0.00009576759,0.0002274425,0.0002686506,0.0001205511],"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":[0.0004030473,0.0003926896,0.001549704,0.0003407688,0.0002402596,0.0004695529,0.001325044,0.7318091,0.03646794,0.02085556,0.004662604,0.2014837],"study_design_scores_gemma":[0.000008770001,0.00002183372,0.00005987901,0.000005056487,0.00001056829,0.00001855818,0.00002492895,0.9920222,0.002482798,0.004779813,0.0005575623,0.00000810927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03063626,0.00015956,0.9621503,0.0001659702,0.00005901654,0.00009642297,0.0001307365,0.004947059,0.001654687],"genre_scores_gemma":[0.6042902,0.0001669144,0.3884238,0.0002743186,0.00005645283,0.0003704077,0.0009096847,0.000541444,0.004966798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003029289,"threshold_uncertainty_score":0.009887516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06428255094117762,"score_gpt":0.2761510849675109,"score_spread":0.2118685340263333,"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."}}