{"id":"W3176765152","doi":"10.18653/v1/2021.acl-long.465","title":"Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Rewriting; Computer science; Natural language generation; Programming language; Computational linguistics; Linguistics; Natural language processing; Library science; Natural language; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004648876,0.0001189033,0.0001280939,0.00001621045,0.0003618786,0.0004756014,0.0004117397,0.000061823,0.00002985448],"category_scores_gemma":[0.0002088781,0.00008654461,0.00005394552,0.0002544495,0.00001353481,0.0007139862,0.0002019938,0.00009099555,0.00002512748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006653283,"about_ca_system_score_gemma":0.00005544717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002947426,"about_ca_topic_score_gemma":0.000103725,"domain_scores_codex":[0.9985315,0.000186476,0.0002982655,0.0004284392,0.0002960084,0.0002592939],"domain_scores_gemma":[0.9990271,0.0001117087,0.00007958315,0.0005028994,0.0002430719,0.00003561979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000182059,0.00005003648,0.0002372457,0.0000159476,0.00002895331,0.0000173174,0.000504792,0.001980411,0.1427414,0.766256,0.06555087,0.0226152],"study_design_scores_gemma":[0.0003379389,0.0000127359,0.00003350533,0.0000244677,0.000007882734,0.00002437503,0.0001666715,0.9501904,0.03650087,0.0008886807,0.01165039,0.0001621419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008887035,0.0005801247,0.9718915,0.008967867,0.0004560498,0.0001178359,7.773951e-7,0.0001921555,0.008906656],"genre_scores_gemma":[0.6214283,0.00007193322,0.3719907,0.002487146,0.0005609713,0.00002482751,0.00001444867,0.00001186869,0.003409775],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9482099,"threshold_uncertainty_score":0.4586235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0411296921574314,"score_gpt":0.2590174505973326,"score_spread":0.2178877584399012,"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."}}