{"id":"W3177367299","doi":"10.1609/aaai.v35i16.17642","title":"Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Transformer; Security token; Encoder; Inference; Parsing; ENCODE; Artificial intelligence; F1 score; Benchmark (surveying); Speech recognition; Natural language processing; Pattern recognition (psychology); Voltage","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":[0.0005045063,0.0001628034,0.0002212738,0.000131655,0.0001459532,0.0001855906,0.0005856947,0.00008216607,0.0000113477],"category_scores_gemma":[0.0004064645,0.000136361,0.00008365598,0.0004461452,0.00007549227,0.000425902,0.0001278954,0.0002444478,0.000005438408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006747433,"about_ca_system_score_gemma":0.00009770325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002851029,"about_ca_topic_score_gemma":0.0001158629,"domain_scores_codex":[0.9985238,0.00001561726,0.000474661,0.0004963211,0.0001964375,0.0002931811],"domain_scores_gemma":[0.9989656,0.0001717736,0.000145536,0.0001713264,0.0004872889,0.00005849849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003420704,0.00009748689,0.0001123046,0.0001280077,0.00001003183,5.152621e-7,0.003893361,0.0001293954,0.3006425,0.4713386,0.000002100308,0.2236116],"study_design_scores_gemma":[0.00003151082,0.00005710853,0.00006224894,0.0002456183,0.000006780107,0.000007201424,0.001000881,0.3495351,0.6012442,0.04765255,0.00003879714,0.0001180104],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3426771,0.000118568,0.6502823,0.001775261,0.0005828312,0.0004441869,0.000001711784,0.00005221098,0.004065791],"genre_scores_gemma":[0.9932143,0.00007294273,0.006441728,0.00007504772,0.00004527537,0.00004934326,1.3632e-7,0.000009152495,0.00009207622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6505371,"threshold_uncertainty_score":0.5560639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.103344581864608,"score_gpt":0.3068403073865154,"score_spread":0.2034957255219074,"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."}}