{"id":"W2597891111","doi":"10.1016/j.csl.2017.01.014","title":"On integrating a language model into neural machine translation","year":2017,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Machine translation; Computer science; Artificial intelligence; Phrase; Natural language processing; Translation (biology); BLEU; Language model; Baseline (sea); Example-based machine translation; Turkish; Machine learning; Linguistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001580626,0.0008293398,0.001050637,0.0008156752,0.0007667691,0.001654843,0.00139461,0.001779214,0.005599578],"category_scores_gemma":[0.005474296,0.0005853667,0.001006023,0.001301334,0.0005760965,0.004170944,0.001630719,0.001774339,0.002749508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000619694,"about_ca_system_score_gemma":0.001095998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007235065,"about_ca_topic_score_gemma":0.01184044,"domain_scores_codex":[0.9993325,0.0002662366,0.00004917617,0.0001589335,0.0001319987,0.00006114613],"domain_scores_gemma":[0.9979431,0.001263924,0.00007513475,0.0002777515,0.0003819713,0.00005802111],"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":[0.0004734967,0.0002915599,0.001396484,0.0002555763,0.0002559842,0.0002897735,0.0001928535,0.2754611,0.01317198,0.04453676,0.01200906,0.6516653],"study_design_scores_gemma":[0.00002113846,0.00004893927,0.0001399721,0.00001506933,0.00004655597,0.00005042092,0.00002376293,0.9595279,0.002721273,0.03542624,0.001964575,0.00001416443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02072159,0.001091579,0.9664782,0.00137032,0.000439773,0.00008953134,0.0002617546,0.004387659,0.005159703],"genre_scores_gemma":[0.377238,0.001454372,0.6033342,0.001269799,0.0004483782,0.0002052399,0.001244945,0.0009349189,0.01387014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007235065,"threshold_uncertainty_score":0.01873249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01622676902070584,"score_gpt":0.3056925943937503,"score_spread":0.2894658253730444,"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."}}