{"id":"W4226059645","doi":"10.1162/tacl_a_00471","title":"TopiOCQA: Open-domain Conversational Question Answering with Topic Switching","year":2022,"lang":"en","type":"article","venue":"Transactions of the Association for Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Minnow Environmental (Canada); Research Canada; Microsoft (Canada); McGill University","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Conversation; Computer science; Question answering; Open domain; Domain (mathematical analysis); Information retrieval; Interdependence; Natural language processing; Artificial intelligence; Relevance (law); Code (set theory); Linguistics; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.004193034,0.0024624,0.001371907,0.003432803,0.001971981,0.002659023,0.004410965,0.003063141,0.007699486],"category_scores_gemma":[0.02006466,0.0006903227,0.001921593,0.002437632,0.0009859382,0.005132341,0.005119776,0.003513007,0.006802582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002440001,"about_ca_system_score_gemma":0.003103758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03986457,"about_ca_topic_score_gemma":0.04586887,"domain_scores_codex":[0.9944724,0.002381542,0.0003820398,0.001635082,0.0007629418,0.0003658794],"domain_scores_gemma":[0.9894555,0.005302078,0.0004267942,0.002410132,0.001617444,0.0007880501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00328068,0.001823583,0.01207427,0.004908916,0.0006358749,0.00101279,0.003071272,0.03275208,0.02015776,0.008756197,0.6344944,0.2770321],"study_design_scores_gemma":[0.001029072,0.0008591131,0.01538516,0.0004876065,0.0003217899,0.00119044,0.002260828,0.6801566,0.02359129,0.02129519,0.2530479,0.0003750591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2107721,0.01237772,0.2166013,0.005104836,0.001738594,0.005086524,0.3328103,0.1897411,0.02576763],"genre_scores_gemma":[0.291302,0.0008820947,0.2051602,0.001781717,0.000392421,0.003031041,0.4882387,0.001571522,0.007640294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03986457,"threshold_uncertainty_score":0.07926506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01518758435261074,"score_gpt":0.2579240160992712,"score_spread":0.2427364317466605,"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."}}