{"id":"W3119707607","doi":"","title":"Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora","year":2020,"lang":"en","type":"article","venue":"Joint Conference on Lexical and Computational Semantics","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Natural language processing; Word (group theory); Lexicon; Artificial intelligence; Benchmark (surveying); Similarity (geometry); Resource (disambiguation); Joint (building); Linguistics","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":[0.003051716,0.002179705,0.001399368,0.001909005,0.0007704877,0.001682257,0.002076668,0.001520788,0.005011544],"category_scores_gemma":[0.01006387,0.0009860059,0.001428246,0.00229947,0.0008997087,0.006867378,0.004023947,0.002980477,0.004888896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006053852,"about_ca_system_score_gemma":0.001414894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002346307,"about_ca_topic_score_gemma":0.004596086,"domain_scores_codex":[0.9978184,0.0007465808,0.0001895394,0.0008346808,0.0002591512,0.0001516635],"domain_scores_gemma":[0.9958482,0.00178726,0.0001812878,0.001197249,0.000830165,0.0001558735],"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":[0.0004236304,0.0004981661,0.004489337,0.0003616125,0.0003236467,0.0002358652,0.0004181476,0.07250282,0.01418655,0.00913809,0.01244821,0.8849739],"study_design_scores_gemma":[0.00005658681,0.0002277425,0.001025102,0.00004690339,0.00008817858,0.0001708866,0.0002733325,0.9537618,0.01002404,0.02534433,0.008940029,0.00004094367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04048733,0.0008258701,0.9499913,0.0002401014,0.0002155276,0.000134534,0.0005178139,0.005017128,0.002570402],"genre_scores_gemma":[0.394406,0.0006998329,0.5845487,0.000512101,0.0002327871,0.0007495706,0.00958028,0.001329477,0.007941328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005011544,"threshold_uncertainty_score":0.01676536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09927202779614587,"score_gpt":0.2928176832555031,"score_spread":0.1935456554593573,"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."}}