{"id":"W2756726036","doi":"10.18653/v1/w17-4732","title":"NRC Machine Translation System for WMT 2017","year":2017,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada); National Research Council Canada","funders":"","keywords":"Machine translation; Computer science; Translation (biology); Machine translation system; Artificial intelligence; Natural language processing; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004044409,0.001820532,0.001618206,0.002410653,0.00237385,0.002081521,0.00209485,0.001575001,0.03590139],"category_scores_gemma":[0.007975426,0.0006381025,0.0008229931,0.002304569,0.0006559248,0.002517029,0.002905754,0.002228485,0.05916378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00211535,"about_ca_system_score_gemma":0.007252399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02356866,"about_ca_topic_score_gemma":0.03559486,"domain_scores_codex":[0.9967816,0.0005897281,0.0003232406,0.0007487519,0.001196263,0.0003605336],"domain_scores_gemma":[0.9946423,0.0004069757,0.0001811912,0.001608985,0.002744826,0.0004158389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005400531,0.0004121312,0.001910339,0.0008676982,0.00009486777,0.0007494921,0.001198048,0.001369322,0.02642152,0.004047533,0.7315046,0.2308844],"study_design_scores_gemma":[0.0003648745,0.0005370548,0.007050272,0.0001782395,0.0001081441,0.001846974,0.0006288082,0.02128215,0.0467283,0.003457242,0.9175649,0.000253029],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.1105904,0.00369491,0.1670206,0.00317278,0.004989375,0.006531881,0.1884655,0.3207047,0.1948298],"genre_scores_gemma":[0.1470587,0.000828652,0.2738267,0.001318872,0.0004363919,0.004285586,0.4802974,0.01388504,0.07806267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03590139,"threshold_uncertainty_score":0.1201022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03566946681279727,"score_gpt":0.3180401626370161,"score_spread":0.2823706958242189,"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."}}