{"id":"W2006617528","doi":"10.3115/1626431.1626467","title":"Improving Arabic-Chinese statistical machine translation using English as pivot language","year":2009,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada","keywords":"Machine translation; Computer science; Natural language processing; Phrase; Arabic; Evaluation of machine translation; Artificial intelligence; Machine translation software usability; Sentence; Example-based machine translation; Translation (biology); Transfer-based machine translation; Speech recognition; Linguistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001684214,0.001091789,0.0008679227,0.0009580416,0.000799584,0.0009673703,0.000505791,0.0003740803,0.003641232],"category_scores_gemma":[0.00491926,0.0003024069,0.000393536,0.001459849,0.0003484918,0.0008608921,0.0009168871,0.0005498946,0.003089823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004867502,"about_ca_system_score_gemma":0.001654692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00664054,"about_ca_topic_score_gemma":0.01104825,"domain_scores_codex":[0.998939,0.0004301113,0.0001273972,0.0001450708,0.0002721162,0.00008627518],"domain_scores_gemma":[0.9976356,0.0007857831,0.0001184959,0.0003350143,0.00104964,0.00007552382],"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":[0.001095603,0.0003091781,0.008097642,0.0009070542,0.0001990694,0.0006408652,0.0008295362,0.03857573,0.1181829,0.006752805,0.02025895,0.8041507],"study_design_scores_gemma":[0.0004355875,0.001149576,0.01325338,0.0001079002,0.0005467301,0.001365176,0.0005997445,0.4840753,0.4316606,0.006352293,0.0602612,0.0001925009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2926747,0.002892226,0.669053,0.0008090435,0.0005873356,0.0003708889,0.001324847,0.01384345,0.01844458],"genre_scores_gemma":[0.5204266,0.0009513295,0.4686498,0.0002652543,0.0001723583,0.0001588351,0.002435141,0.0006383569,0.006302382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00664054,"threshold_uncertainty_score":0.0132038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009009416888915548,"score_gpt":0.2929979197001801,"score_spread":0.2839885028112645,"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."}}