{"id":"W2970680405","doi":"10.18653/v1/d19-1201","title":"Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders","year":2019,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; National Science Foundation","keywords":"Personalization; Computer science; Chen; Joint (building); Space (punctuation); Artificial intelligence; Natural language processing; World Wide Web; Engineering; Operating system","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":[0.001278855,0.0009905546,0.001081326,0.0004510876,0.0003417281,0.0008632495,0.001375751,0.001245762,0.00298473],"category_scores_gemma":[0.003272035,0.0007050027,0.001044421,0.0005742361,0.0007399409,0.001533788,0.001466802,0.002209446,0.001045117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000695587,"about_ca_system_score_gemma":0.0007119302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007736135,"about_ca_topic_score_gemma":0.009892107,"domain_scores_codex":[0.9994698,0.0001710032,0.00002313511,0.0001825843,0.00007428511,0.00007931664],"domain_scores_gemma":[0.998888,0.0007032445,0.00008411646,0.0001100725,0.0001547477,0.00005987428],"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":[0.0002629694,0.0001237854,0.001382348,0.00009566121,0.0001117593,0.000108849,0.0001862171,0.7888564,0.00571966,0.009677378,0.003555115,0.1899199],"study_design_scores_gemma":[0.000003343568,0.000009590831,0.00006456347,0.000002499702,0.000004761445,0.000007013604,0.000003427098,0.9980477,0.0002739823,0.001439278,0.0001409596,0.000002878073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01999865,0.000527666,0.9774154,0.0002235937,0.00008611636,0.00003019661,0.00008488999,0.0007272155,0.0009061955],"genre_scores_gemma":[0.7966493,0.0005309872,0.1921002,0.0003356371,0.0001518728,0.0002006672,0.0004970513,0.000362939,0.009171365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007736135,"threshold_uncertainty_score":0.01538223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0294755678688546,"score_gpt":0.2524900829017831,"score_spread":0.2230145150329285,"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."}}