{"id":"W3099289136","doi":"10.18653/v1/2020.findings-emnlp.142","title":"Neural Dialogue State Tracking with Temporally Expressive Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Beijing Advanced Innovation Center for Big Data and Brain Computing; Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Computer science; Graphical model; Expressive power; State (computer science); Probabilistic logic; Tracking (education); Feature (linguistics); Artificial intelligence; Recurrent neural network; Artificial neural network; Natural language processing; Machine learning; Theoretical computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000882582,0.0001478081,0.0001816118,0.0000271488,0.00007540535,0.0002807605,0.0006258885,0.00003701149,0.00001013759],"category_scores_gemma":[0.00002353893,0.0001040856,0.00004381694,0.0003170033,0.00003007769,0.0006008939,0.0001358339,0.0001325716,0.00004524928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001001509,"about_ca_system_score_gemma":0.00005181773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001107938,"about_ca_topic_score_gemma":0.00004610445,"domain_scores_codex":[0.9988005,0.00006543515,0.000193779,0.0003883147,0.000224955,0.0003270694],"domain_scores_gemma":[0.9992347,0.00006129833,0.0000923863,0.0002920459,0.00006715713,0.0002524429],"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":[0.001225532,0.0004519362,0.1032473,0.000252335,0.000497485,0.00622548,0.04051546,0.3138747,0.01269316,0.04103383,0.1690425,0.3109403],"study_design_scores_gemma":[0.002197305,0.001128018,0.008439385,0.00005669722,0.00001126796,0.00008919055,0.0002318814,0.9745616,0.006039096,0.0007767727,0.005473626,0.0009951729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01946308,0.0001002855,0.9726985,0.002311114,0.0003046814,0.0002377106,0.000002244969,0.0004578608,0.004424524],"genre_scores_gemma":[0.988099,0.000002364496,0.008577924,0.002956128,0.0002474712,0.00001128381,0.000005829217,0.0000113486,0.00008867331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9686359,"threshold_uncertainty_score":0.4244489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02215996736713131,"score_gpt":0.2096675132888617,"score_spread":0.1875075459217304,"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."}}