{"id":"W2036126214","doi":"10.1016/j.csl.2014.10.007","title":"Hybrid Arabic–French machine translation using syntactic re-ordering and morphological pre-processing","year":2014,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Machine translation; Natural language processing; Artificial intelligence; Example-based machine translation; Arabic; BLEU; Translation (biology); Verb; Linguistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0007085794,0.00222655,0.001369747,0.002191574,0.001327633,0.002953601,0.0008846805,0.001138978,0.01675704],"category_scores_gemma":[0.002084311,0.0005997319,0.001362547,0.001703214,0.000438083,0.001415703,0.00147589,0.001309818,0.01426248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005694743,"about_ca_system_score_gemma":0.001666766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004816342,"about_ca_topic_score_gemma":0.007711682,"domain_scores_codex":[0.9990181,0.0002853816,0.0001159221,0.0002702379,0.0001902733,0.0001201511],"domain_scores_gemma":[0.9982003,0.0004387685,0.00008760811,0.0003346507,0.0008772926,0.00006133596],"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":[0.001080135,0.0003495765,0.001618792,0.001359012,0.0003555451,0.002874835,0.0009090123,0.0108733,0.1860245,0.01282139,0.0428963,0.7388375],"study_design_scores_gemma":[0.0004673254,0.001093568,0.006548847,0.0003466012,0.001010402,0.004979133,0.001968993,0.3327609,0.3833032,0.02520008,0.2418425,0.0004784725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08576951,0.002110946,0.8244672,0.001102872,0.002048714,0.0005450099,0.005007947,0.04509246,0.03385535],"genre_scores_gemma":[0.25204,0.0008393508,0.7147599,0.0004747667,0.0004178812,0.0002566048,0.009766093,0.004262144,0.01718323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01675704,"threshold_uncertainty_score":0.05605787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.016871099164201,"score_gpt":0.2750535633622381,"score_spread":0.2581824641980371,"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."}}