{"id":"W2399456070","doi":"10.21437/interspeech.2013-596","title":"Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding","year":2013,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":432,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Spoken language; Artificial neural network; Natural language processing; Recurrent neural network","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":[0.0004887004,0.00006259464,0.0000977091,0.00005047934,0.00007740417,0.00006061743,0.0001426666,0.00002804179,0.000007598853],"category_scores_gemma":[0.0001318448,0.00005174798,0.00002332859,0.0001029548,0.0000262026,0.0001169257,0.00009369488,0.00009387323,4.829901e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001385295,"about_ca_system_score_gemma":0.000009852108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006400818,"about_ca_topic_score_gemma":0.000005767933,"domain_scores_codex":[0.9993488,0.0001311314,0.0001378683,0.0001785044,0.00006257564,0.0001411586],"domain_scores_gemma":[0.9992325,0.0005160402,0.00006674596,0.0001177201,0.00002165319,0.00004533132],"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":[0.000005489824,0.000004375755,0.009897255,0.000146384,0.00003002746,3.69042e-7,0.01221668,0.01635663,0.01040052,0.107166,0.0002166173,0.8435597],"study_design_scores_gemma":[0.0001322215,0.00004114776,0.000909274,0.0000319827,0.000003820743,0.000002244503,0.000351086,0.9477496,0.001882042,0.04879839,0.00002195017,0.0000762324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2318305,0.0001664849,0.7672009,0.0003058575,0.00007174395,0.0001549203,7.154e-8,0.00005248584,0.0002170044],"genre_scores_gemma":[0.5112598,0.000001426335,0.4885896,0.00005459411,0.00002830376,0.000007138741,4.972676e-7,0.000002744172,0.00005583945],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.931393,"threshold_uncertainty_score":0.2110221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0788804892206361,"score_gpt":0.329259228172978,"score_spread":0.2503787389523419,"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."}}