{"id":"W4285105102","doi":"10.18653/v1/2022.acl-long.17","title":"CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Computer science; Ciphertext; Machine translation; Plaintext; Artificial neural network; Leverage (statistics); Artificial intelligence; Source code; Transformer; Algorithm; Encryption; Programming language","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":[0.001661792,0.001202698,0.0009885291,0.0008206535,0.0007107859,0.001065294,0.001985325,0.001013443,0.004616004],"category_scores_gemma":[0.006090117,0.0004706703,0.0008990005,0.001415835,0.001302677,0.002963926,0.003267828,0.002611245,0.003396048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006290544,"about_ca_system_score_gemma":0.001298687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001187619,"about_ca_topic_score_gemma":0.001879642,"domain_scores_codex":[0.9987312,0.0004768834,0.000101302,0.0002869475,0.0003131901,0.00009042932],"domain_scores_gemma":[0.9972916,0.0008400275,0.0001709095,0.001191423,0.000420292,0.00008562953],"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":[0.001145921,0.0005712493,0.001869507,0.000540751,0.000159384,0.000378132,0.0004657957,0.1192397,0.08078738,0.04520455,0.02243776,0.7271999],"study_design_scores_gemma":[0.0001021706,0.0003219135,0.0004606819,0.00005201502,0.00003923567,0.0003046941,0.00007891229,0.8525085,0.0819793,0.04258307,0.02151023,0.0000592171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01841842,0.0004052379,0.9714588,0.0003358786,0.0002427782,0.0001651013,0.0006552745,0.005490857,0.002827661],"genre_scores_gemma":[0.2986369,0.0004240813,0.6883707,0.0004464133,0.0001987631,0.00068152,0.003710118,0.0008083293,0.006723148],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004616004,"threshold_uncertainty_score":0.01544201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925407382792177,"score_gpt":0.2733220621644697,"score_spread":0.2540679883365479,"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."}}