{"id":"W4385701337","doi":"10.1007/978-3-031-39831-5_22","title":"Exploring Dialog Act Recognition in Open Domain Conversational Agents","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Dialog box; Computer science; Classifier (UML); Open domain; Dialog system; Artificial intelligence; Support vector machine; Domain (mathematical analysis); Baseline (sea); Natural language processing; Machine learning; Speech recognition; World Wide Web; Question answering","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":[0.001150085,0.0007458907,0.0009039686,0.000580387,0.0007256005,0.002682124,0.00124602,0.00125687,0.003737808],"category_scores_gemma":[0.004148556,0.0005153109,0.0008783203,0.0005098347,0.000505286,0.002728734,0.001978709,0.001826277,0.001126497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005084242,"about_ca_system_score_gemma":0.0005336376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003484428,"about_ca_topic_score_gemma":0.003704902,"domain_scores_codex":[0.999078,0.0004156596,0.0000361891,0.000251797,0.0001125702,0.0001057891],"domain_scores_gemma":[0.9977016,0.001876821,0.00008045079,0.0001262736,0.0001297102,0.00008512755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001654078,0.0007759254,0.007934337,0.0005856426,0.0003013044,0.0007071377,0.00466552,0.09678926,0.06041487,0.03048665,0.009527355,0.786158],"study_design_scores_gemma":[0.00001696724,0.00007936852,0.001532389,0.00003082408,0.00004141936,0.0001113269,0.0008280483,0.9600434,0.00902204,0.02475652,0.003513439,0.00002420358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1548381,0.001385764,0.8321066,0.0005082978,0.0001086573,0.0001350778,0.0005036707,0.003635698,0.006778151],"genre_scores_gemma":[0.7851796,0.0002958125,0.207767,0.0001194812,0.00006423445,0.0001071453,0.001161749,0.0002806582,0.005024408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003737808,"threshold_uncertainty_score":0.01250422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1822798249837222,"score_gpt":0.2925068874468192,"score_spread":0.110227062463097,"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."}}