{"id":"W2809215328","doi":"10.1109/compsac.2018.00113","title":"Long Short-Term Memory Neural Networks for Artificial Dialogue Generation","year":2018,"lang":"en","type":"article","venue":"","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Hidden Markov model; Encoder; Artificial neural network; Artificial intelligence; Sequence (biology); Recurrent neural network; Term (time); Decoding methods; Long short term memory; Architecture; Speech recognition; Natural language processing; Algorithm","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":[0.001400711,0.0007009252,0.0004375585,0.0004205013,0.0003055755,0.0007631188,0.0008723272,0.0009231441,0.002573283],"category_scores_gemma":[0.004061238,0.0003343659,0.0004140373,0.0006004302,0.0003920947,0.0013773,0.0005198417,0.00144837,0.0006438295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009703084,"about_ca_system_score_gemma":0.0006189092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006791858,"about_ca_topic_score_gemma":0.009113762,"domain_scores_codex":[0.9995347,0.0002198826,0.00003074292,0.0001052314,0.00007191266,0.00003744246],"domain_scores_gemma":[0.9985813,0.001058853,0.00006273024,0.00008854467,0.000182212,0.00002635012],"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":[0.0002440027,0.0001364793,0.0009446586,0.0003125667,0.0001219645,0.0001618607,0.0002367867,0.6671262,0.0121421,0.01031282,0.002364847,0.3058957],"study_design_scores_gemma":[0.000004589494,0.00002807572,0.0001405108,0.0000118307,0.000008775486,0.00001290289,0.0000104741,0.9943282,0.001757451,0.003108857,0.0005820282,0.000006385533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0498096,0.002742836,0.9402937,0.0004889783,0.0001810777,0.0001064312,0.0003259991,0.002624799,0.003426692],"genre_scores_gemma":[0.7002001,0.001036768,0.2926838,0.0002204154,0.00007686599,0.0003171595,0.0007296873,0.0001724731,0.00456275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006791858,"threshold_uncertainty_score":0.01350462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05047732032030343,"score_gpt":0.2742333457109574,"score_spread":0.2237560253906539,"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."}}