{"id":"W2408742823","doi":"10.2495/dne-v11-n2-88-96","title":"A multi-agent solution for managing complexity in english to sinhala machine translation","year":2016,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Syntax; Artificial intelligence; Machine translation; Natural language processing; Semantics (computer science); Sentence; Transfer-based machine translation; Rule-based machine translation; Process (computing); Point (geometry); Ontology; Translation (biology); Example-based machine translation; Programming language","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":[0.001104593,0.0006725822,0.0006073383,0.0003624869,0.001656275,0.001749684,0.001146115,0.001112105,0.002367198],"category_scores_gemma":[0.001859869,0.0003722519,0.0006430997,0.0003484217,0.0006978196,0.001203244,0.002318284,0.001124342,0.0004823079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007220041,"about_ca_system_score_gemma":0.001832479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003743372,"about_ca_topic_score_gemma":0.004589738,"domain_scores_codex":[0.9993501,0.000267618,0.00005952999,0.0001365342,0.0001187268,0.00006739561],"domain_scores_gemma":[0.9992815,0.0002812433,0.00007810471,0.00007918396,0.0001927707,0.00008727614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004337068,0.0004399277,0.002685091,0.0006116888,0.000298844,0.002536494,0.003145526,0.5506529,0.03153028,0.1195675,0.008394883,0.2797032],"study_design_scores_gemma":[0.00009812734,0.0001642156,0.00035975,0.00003195507,0.00007349285,0.0002407055,0.0003828686,0.9558085,0.004261409,0.0206398,0.01789875,0.00004056204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05084334,0.0003936074,0.9335625,0.001098725,0.000134456,0.0003251375,0.00004705846,0.0008092437,0.01278591],"genre_scores_gemma":[0.4743438,0.0002813948,0.5131823,0.0002202452,0.00004916073,0.0005327832,0.0001007548,0.00008779475,0.01120182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003743372,"threshold_uncertainty_score":0.007919073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03216539525153374,"score_gpt":0.3047113446003997,"score_spread":0.2725459493488659,"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."}}