{"id":"W3105160568","doi":"10.18653/v1/2020.findings-emnlp.259","title":"Inferring symmetry in natural language","year":2020,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Predicate (mathematical logic); Computer science; Natural language processing; Inference; Artificial intelligence; Symmetry (geometry); Context (archaeology); Natural language; Sentence; Linguistics; Mathematics; Programming language; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004719189,0.00003777822,0.00005103523,0.00002976565,0.000009349525,0.00003772536,0.0003279154,0.00001475235,0.00001225669],"category_scores_gemma":[0.00003656973,0.00003312613,0.00001388267,0.00018638,0.000002782254,0.000194397,0.0001904705,0.00009889355,0.00004077808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001103604,"about_ca_system_score_gemma":0.00001091437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006748793,"about_ca_topic_score_gemma":0.00002078748,"domain_scores_codex":[0.9995811,0.00001021104,0.00007770378,0.0001483701,0.00007854928,0.0001040781],"domain_scores_gemma":[0.9997959,0.00001730564,0.000009042201,0.000136057,0.000005062273,0.00003668388],"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":[0.000003981269,0.00003126495,0.03269886,0.00005466345,0.0000108912,0.0002666384,0.02044925,0.001704882,0.00992558,0.4450564,0.0005816986,0.4892159],"study_design_scores_gemma":[0.0001357523,0.000006294642,0.002622636,0.000005280339,2.580991e-7,0.000002050813,0.0001182138,0.9948118,0.001717347,0.0002403876,0.0002645383,0.00007540883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3825327,0.000280647,0.6001304,0.004186533,0.0001683031,0.00004836917,6.960862e-8,0.0002299854,0.01242301],"genre_scores_gemma":[0.954248,5.547864e-7,0.04339161,0.002232373,0.00004172077,7.821478e-7,1.277808e-7,0.00000178228,0.00008309288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.993107,"threshold_uncertainty_score":0.1350845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01830866518192214,"score_gpt":0.2502839441756352,"score_spread":0.231975278993713,"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."}}