{"id":"W2022433169","doi":"10.3115/1626355.1626383","title":"Rule-based translation with statistical phrase-based post-editing","year":2007,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Machine translation; Phrase; Natural language processing; Artificial intelligence; Synchronous context-free grammar; Task (project management); Translation (biology); Example-based machine translation; Machine translation software usability; Transfer-based machine translation; Machine translation system; Evaluation of machine translation; Programming language; 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":[0.0004809277,0.000131385,0.0001070935,0.0001394273,0.00009495096,0.0001377807,0.0003771143,0.00006444351,0.00004336752],"category_scores_gemma":[0.00005990656,0.00009672812,0.0000241379,0.0003377979,0.00005438313,0.0003235961,0.00001642994,0.0001628989,0.000009822949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004121057,"about_ca_system_score_gemma":0.0001177668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006066589,"about_ca_topic_score_gemma":0.00007031865,"domain_scores_codex":[0.9988152,0.00003005878,0.0001880929,0.0003002564,0.0003726034,0.0002937888],"domain_scores_gemma":[0.9991593,0.0002705553,0.00006525829,0.0002582443,0.0001515664,0.00009510104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001554068,0.0001619554,0.0009273744,0.00009780764,0.00001013866,0.0002909137,0.0002079525,0.00007520798,0.02137041,0.09257648,0.0002128804,0.8839135],"study_design_scores_gemma":[0.002076405,0.000716753,0.001981476,0.0001986431,0.00002596125,0.00003589849,0.00004344079,0.3097362,0.6712254,0.01210824,0.001008404,0.0008431814],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002669653,0.00009784321,0.9937309,0.001289051,0.00004173441,0.0001413956,0.000004980831,0.001109534,0.0009149305],"genre_scores_gemma":[0.4397672,3.548241e-8,0.5592605,0.0009115563,0.00002693645,0.00000303909,0.00001398233,0.000006718445,0.00001005374],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8830703,"threshold_uncertainty_score":0.3944458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273436613526829,"score_gpt":0.2741413332576405,"score_spread":0.2614069671223722,"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."}}