{"id":"W3201759467","doi":"","title":"TopiOCQA: Open-domain Conversational Question Answeringwith Topic Switching.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Conversation; Computer science; Question answering; Domain (mathematical analysis); Natural language processing; Open domain; Interdependence; Artificial intelligence; Information retrieval; Code (set theory); Relevance (law); Limiting; Linguistics; Set (abstract data type); Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003079732,0.002741763,0.001247182,0.003804561,0.001836661,0.002523021,0.004044382,0.003319983,0.008638998],"category_scores_gemma":[0.01911917,0.0006471163,0.001642426,0.002724874,0.0008515583,0.005298516,0.005249867,0.003058212,0.009745427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002271474,"about_ca_system_score_gemma":0.00324343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03139611,"about_ca_topic_score_gemma":0.04856079,"domain_scores_codex":[0.9955948,0.001751579,0.0003475764,0.001292872,0.0007012881,0.0003117873],"domain_scores_gemma":[0.9926969,0.003338097,0.0004260672,0.001714019,0.001224002,0.0006008712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001623346,0.001093358,0.008567476,0.005481571,0.0004423574,0.0006368675,0.002381132,0.01055848,0.01334874,0.006655249,0.7940274,0.1551839],"study_design_scores_gemma":[0.001008847,0.0008104143,0.02239757,0.000711528,0.0003304129,0.00180115,0.002990963,0.2899735,0.02822966,0.02667008,0.6246594,0.0004164714],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.08935883,0.008804669,0.1126787,0.002946492,0.00120249,0.004454778,0.6593261,0.09571894,0.02550904],"genre_scores_gemma":[0.1049887,0.000710167,0.1150384,0.001169711,0.00022516,0.002988358,0.7666423,0.0009872145,0.007249975],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03139611,"threshold_uncertainty_score":0.06242675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06853225001111356,"score_gpt":0.2029563697949861,"score_spread":0.1344241197838726,"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."}}