{"id":"W3177337627","doi":"10.1609/aaai.v35i14.17524","title":"Analogy Training Multilingual Encoders","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"H. Lundbeck A/S; Natural Sciences and Engineering Research Council of Canada; Lundbeckfonden","keywords":"Analogy; Computer science; Natural language processing; Encoder; Sentence; Artificial intelligence; Word (group theory); Encoding (memory); Linguistics","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":[0.0004387881,0.000189921,0.0002653622,0.00009649614,0.0001759508,0.0002080908,0.001706796,0.00009472785,0.00007045508],"category_scores_gemma":[0.0007491615,0.0001571274,0.0001346765,0.0006126571,0.0001739508,0.0003165197,0.0004496891,0.0003315251,0.00004126329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003720099,"about_ca_system_score_gemma":0.0002898224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002738474,"about_ca_topic_score_gemma":0.00002459593,"domain_scores_codex":[0.9980091,0.00002282666,0.0005253415,0.0006013084,0.0004529323,0.0003885013],"domain_scores_gemma":[0.9984453,0.0001087458,0.0002488156,0.0003788213,0.0007240042,0.00009428006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008845735,0.00005994416,0.00009815551,0.00001730246,0.00001240386,0.000002783275,0.004752901,0.0003014136,0.04540423,0.7605159,0.00002173353,0.1888043],"study_design_scores_gemma":[0.00002627746,0.0000541046,0.00005940176,0.0001236305,0.000008045209,0.00001767358,0.002476077,0.2505508,0.5904493,0.1559134,0.0001261502,0.0001951182],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5682774,0.00009105004,0.3762353,0.009979032,0.00177073,0.0003909176,0.000004803252,0.0002625468,0.04298824],"genre_scores_gemma":[0.9787753,0.00001914632,0.02043881,0.000390672,0.00008604793,0.000009217806,2.957931e-7,0.00000957627,0.0002709117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6046026,"threshold_uncertainty_score":0.6407468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1549327852651082,"score_gpt":0.3235714892127046,"score_spread":0.1686387039475964,"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."}}