{"id":"W3120950293","doi":"10.18653/v1/2020.aacl-srw.14","title":"Training with Adversaries to Improve Faithfulness of Attention in Neural Machine Translation","year":2020,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Machine translation; Computer science; Regularization (linguistics); Measure (data warehouse); Translation (biology); Divergence (linguistics); Artificial intelligence; Differentiable function; Machine learning; Quality (philosophy); Trustworthiness; Language model; Natural language processing; Mathematics; Data mining","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":[0.002815044,0.001277559,0.001319829,0.0004830417,0.0006785147,0.0009581859,0.00200656,0.001747293,0.004077398],"category_scores_gemma":[0.01221739,0.0007109834,0.0006551608,0.0005998611,0.001643065,0.003361434,0.003933397,0.003405749,0.001682742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007134118,"about_ca_system_score_gemma":0.0008351687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00195339,"about_ca_topic_score_gemma":0.003122557,"domain_scores_codex":[0.9988181,0.0006223579,0.000063017,0.0002396236,0.0001303578,0.000126536],"domain_scores_gemma":[0.9936458,0.004650726,0.0001981768,0.001006717,0.0003491886,0.0001494028],"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":[0.00110702,0.0003276124,0.001725603,0.0002692415,0.0001743171,0.0003055549,0.0005059388,0.6601695,0.01586773,0.04742041,0.01296448,0.2591625],"study_design_scores_gemma":[0.00002871407,0.00007178295,0.0001092072,0.00001718936,0.00001499417,0.00003162077,0.00001950367,0.9757494,0.002677244,0.02064739,0.0006252896,0.000007876619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09568157,0.002581415,0.8871098,0.001680775,0.0005038372,0.0001087462,0.0002310379,0.004195848,0.007906944],"genre_scores_gemma":[0.9162213,0.0004795919,0.0751099,0.0005981205,0.0002093369,0.0001382755,0.0004362197,0.0005391935,0.006268004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004077398,"threshold_uncertainty_score":0.01488751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04479434594496593,"score_gpt":0.2456234107420521,"score_spread":0.2008290647970862,"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."}}