{"id":"W4283792637","doi":"10.1609/aaai.v36i11.21591","title":"Multi-Dimension Attention for Multi-Turn Dialog Generation (Student Abstract)","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Dialog box; Dimension (graph theory); Computer science; Generative grammar; Scope (computer science); Generative model; Artificial intelligence; Process (computing); Dialog system; Natural language processing; Semantic interpretation; Speech recognition; Programming language; Mathematics; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000778731,0.0001850639,0.0001963953,0.000118465,0.0005628079,0.0002000823,0.00138784,0.00005712505,0.00005248495],"category_scores_gemma":[0.0001715134,0.0001575018,0.0001444258,0.000289179,0.00006654913,0.0003409779,0.000618604,0.0002746194,0.00001751539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001130602,"about_ca_system_score_gemma":0.00007286035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004324988,"about_ca_topic_score_gemma":0.00001959566,"domain_scores_codex":[0.9979896,0.00001980248,0.0005675386,0.0005774852,0.0005482328,0.0002973178],"domain_scores_gemma":[0.9986811,0.00004588161,0.0004325359,0.0002906391,0.0004888549,0.0000609399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003733274,0.0005050025,0.0003686531,0.00003194965,0.00001738439,4.335744e-7,0.002113664,0.003233319,0.6293733,0.3056469,0.000135358,0.05853673],"study_design_scores_gemma":[0.00007982602,0.0001461703,0.0007945315,0.00003232984,0.00001043554,0.000002743306,0.0003892787,0.8155995,0.1760401,0.006649388,0.00007208173,0.0001836059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4811222,0.00003292602,0.5138441,0.001808717,0.001660366,0.001117034,0.00001167569,0.0001128569,0.0002901169],"genre_scores_gemma":[0.9601505,0.000009137199,0.03911115,0.0002025214,0.00009965476,0.0001770178,0.000002588505,0.00001247564,0.000234897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8123662,"threshold_uncertainty_score":0.6422737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2006134807242427,"score_gpt":0.3464957192817173,"score_spread":0.1458822385574746,"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."}}