{"id":"W4379053645","doi":"10.3390/cmsf2023006003","title":"Developing Conversational Agent Using Deep Learning Techniques","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"Centre National pour la Recherche Scientifique et Technique","keywords":"Computer science; Converse; Deep learning; Artificial intelligence; Encoder; Natural language; Recurrent neural network; Architecture; Sequence (biology); Artificial neural network; Natural (archaeology); Natural language processing; Human–computer interaction","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":[0.0008584928,0.0005541537,0.0003966474,0.0002854888,0.0005069569,0.0007328661,0.0008056551,0.0008493051,0.002893948],"category_scores_gemma":[0.0017356,0.0004251792,0.0006904668,0.0001582485,0.0004311225,0.001575538,0.001146385,0.001412942,0.0009118887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006646291,"about_ca_system_score_gemma":0.001153355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004447784,"about_ca_topic_score_gemma":0.006532348,"domain_scores_codex":[0.9996914,0.00009317835,0.00002705987,0.00007731161,0.00007177934,0.000039394],"domain_scores_gemma":[0.9995282,0.0001946579,0.00003398972,0.00005355765,0.0001451763,0.00004452865],"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":[0.0001874695,0.0004802661,0.003032006,0.0003411155,0.0002096233,0.0004951307,0.00127563,0.521758,0.04339546,0.06036817,0.008754224,0.3597029],"study_design_scores_gemma":[0.000006921729,0.00002082134,0.00005011469,0.000007126522,0.00001178087,0.00002117161,0.00003071175,0.987485,0.004584674,0.004664519,0.003111178,0.000006010526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0239947,0.0001770552,0.9681474,0.000345594,0.0000670538,0.0001391756,0.00009474515,0.002537224,0.004497104],"genre_scores_gemma":[0.3616257,0.0002431804,0.6289144,0.0003180838,0.00003576082,0.0003595646,0.0003401252,0.000257828,0.007905297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004447784,"threshold_uncertainty_score":0.009681225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08726983656625664,"score_gpt":0.3092228061348674,"score_spread":0.2219529695686108,"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."}}