{"id":"W1512230024","doi":"10.1109/icassp.1995.479398","title":"Understanding referring expressions in a person-machine spoken dialogue","year":2002,"lang":"en","type":"article","venue":"","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Natural language processing; Speech recognition; Artificial intelligence; Linguistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002114134,0.0001108481,0.0001472746,0.0001986745,0.0001019643,0.0001071446,0.0004660267,0.00005740752,0.0001332258],"category_scores_gemma":[0.00006357113,0.00009123497,0.00004704948,0.00039112,0.00001775593,0.0003880071,0.0001136518,0.0001272478,0.0001631382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001423337,"about_ca_system_score_gemma":0.00001130077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005558608,"about_ca_topic_score_gemma":0.0005649517,"domain_scores_codex":[0.9989534,0.00006245368,0.0001660458,0.000304673,0.0001931194,0.000320317],"domain_scores_gemma":[0.9993474,0.0001091606,0.00003757043,0.0003780487,0.000009545313,0.0001182496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004099626,0.0008415049,0.07342093,0.0001239299,0.00007531785,0.001051701,0.04363761,0.001140872,0.02172728,0.7837265,0.05966072,0.01455261],"study_design_scores_gemma":[0.006161167,0.0003977535,0.01687559,0.0007322967,0.00001408546,0.0002539124,0.006028909,0.927209,0.005254261,0.02471826,0.0100342,0.002320606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01977758,0.0004529803,0.662618,0.001687679,0.0008822893,0.0002919277,0.000003225445,0.0004707332,0.3138156],"genre_scores_gemma":[0.9935138,0.00001268482,0.005415978,0.0001635641,0.00006372778,0.00001052792,0.000001506981,0.000006952773,0.0008113034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9737362,"threshold_uncertainty_score":0.3720454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2227603622286896,"score_gpt":0.2542107702734163,"score_spread":0.03145040804472676,"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."}}