{"id":"W2117827367","doi":"10.3115/1610075.1610084","title":"Phrasetable smoothing for statistical machine translation","year":2006,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Advanced Research Projects Agency; Defense Advanced Research Projects Agency","keywords":"Smoothing; Metric (unit); Machine translation; Computer science; Translation (biology); Range (aeronautics); Yield (engineering); Artificial intelligence; BLEU; Machine learning; Algorithm; Computer vision; Engineering","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":[0.0001575378,0.00006314317,0.00006881047,0.00004264163,0.00007182403,0.0001236684,0.0002474174,0.0000335039,0.00001772672],"category_scores_gemma":[0.00001869571,0.00005077185,0.00002040193,0.0001051007,0.00001166106,0.0003562925,0.00002219537,0.00005319071,0.000003638452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001451935,"about_ca_system_score_gemma":0.00001579885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002052502,"about_ca_topic_score_gemma":0.00004309905,"domain_scores_codex":[0.9994445,0.00001140077,0.0001177701,0.0001738121,0.0001076446,0.000144854],"domain_scores_gemma":[0.9996666,0.0001174335,0.00002551681,0.0001378511,0.00003250765,0.00002005063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004098892,0.00002415222,0.00003822477,0.00002100405,0.000001682934,0.00000259713,0.00003374182,0.0000112638,0.003941872,0.8068464,0.003572033,0.1855029],"study_design_scores_gemma":[0.0002511617,0.0000385456,0.00004209212,0.000008925776,0.000004726481,0.000006618927,0.000001304032,0.4449686,0.03609495,0.5107989,0.007627453,0.0001566268],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003808411,0.0009850938,0.9957562,0.0007593892,0.00004285528,0.0001289556,0.00001027682,0.0005899859,0.001689159],"genre_scores_gemma":[0.192309,5.116893e-7,0.8071951,0.0001632898,0.00003064958,0.00001155359,0.00002410574,0.000004439087,0.0002613412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4449574,"threshold_uncertainty_score":0.2070416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01301703170445941,"score_gpt":0.2779080599458409,"score_spread":0.2648910282413815,"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."}}