{"id":"W3166956191","doi":"","title":"The Impact of Sentence Alignment Errors on Phrase-Based Machine Translation Performance","year":2012,"lang":"en","type":"article","venue":"NPARC","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine translation; Sentence; Phrase; Robustness (evolution); Natural language processing; Artificial intelligence; Speech recognition; Translation (biology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.007204683,0.001163703,0.001017021,0.001038483,0.0009525011,0.002251748,0.0009941771,0.001678993,0.003511532],"category_scores_gemma":[0.06308749,0.0005489407,0.0003752207,0.002610853,0.0008649101,0.004267673,0.001463647,0.001492096,0.003220654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008133441,"about_ca_system_score_gemma":0.0007460489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003641105,"about_ca_topic_score_gemma":0.003818417,"domain_scores_codex":[0.9906086,0.00357145,0.001111923,0.00179645,0.002408431,0.0005032005],"domain_scores_gemma":[0.9317009,0.05489687,0.003369981,0.003905412,0.00564484,0.0004818887],"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":[0.005448003,0.001004279,0.06964412,0.002144476,0.0006947546,0.001221433,0.001830784,0.1712124,0.163861,0.003341852,0.0217815,0.5578155],"study_design_scores_gemma":[0.0001874166,0.004560651,0.1251594,0.0002210831,0.0005198698,0.001672535,0.001200383,0.5360387,0.3129809,0.00839687,0.008703429,0.0003587841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9321532,0.005493465,0.04495256,0.001679709,0.0005305732,0.000102896,0.001927265,0.003838641,0.009321651],"genre_scores_gemma":[0.9675497,0.001080085,0.02553585,0.0002187217,0.0001548592,0.00006612149,0.003065402,0.0005460874,0.001783197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007204683,"threshold_uncertainty_score":0.03810245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01955218665778276,"score_gpt":0.2901371945109736,"score_spread":0.2705850078531908,"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."}}