{"id":"W2760656271","doi":"10.18653/v1/w17-4717","title":"Findings of the 2017 Conference on Machine Translation (WMT17)","year":2017,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":418,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"European Regional Development Fund; Horizon 2020 Framework Programme; Agence Nationale de la Recherche; Univerzita Karlova v Praze; Trinity College Dublin; Science Foundation Ireland; European Commission","keywords":"Chatterjee; Translation (biology); Machine translation; Art history; Computer science; Art; Artificial intelligence; Chemistry","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":[0.030872,0.003786767,0.003194849,0.01469636,0.004098598,0.0128099,0.003553762,0.004305138,0.06335443],"category_scores_gemma":[0.04272645,0.0009041323,0.002477681,0.01119526,0.002889212,0.01054184,0.009254429,0.006633366,0.06948832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004989407,"about_ca_system_score_gemma":0.01438011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01813523,"about_ca_topic_score_gemma":0.02621919,"domain_scores_codex":[0.9782381,0.006434896,0.001598976,0.002060077,0.009632913,0.002035026],"domain_scores_gemma":[0.9385006,0.01107933,0.001567481,0.007313407,0.03304956,0.008489511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003841686,0.0001708449,0.0008766606,0.0005172191,0.00006388574,0.0001451485,0.0001210968,0.0004868272,0.0008799483,0.002714846,0.9385592,0.0550802],"study_design_scores_gemma":[0.0001484242,0.0001524197,0.003742968,0.0006904938,0.0001246968,0.000273318,0.0005522036,0.002363156,0.006834755,0.008806232,0.9762357,0.00007548397],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.02367807,0.08333302,0.05796167,0.2046065,0.255079,0.002390971,0.1988698,0.0130436,0.1610374],"genre_scores_gemma":[0.05277961,0.04301537,0.07751177,0.01922636,0.03631927,0.002679765,0.5380719,0.01099163,0.2194043],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06335443,"threshold_uncertainty_score":0.2119417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04852302411475251,"score_gpt":0.3154419876781849,"score_spread":0.2669189635634324,"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."}}