{"id":"W2950682695","doi":"","title":"An Autoencoder Approach to Learning Bilingual Word Representations","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Autoencoder; Artificial intelligence; Natural language processing; Word (group theory); Classifier (UML); German; Task (project management); Point (geometry); Language model; Deep learning; Linguistics; Mathematics","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":[0.001465056,0.001177736,0.0008971483,0.001052245,0.0003912217,0.0007811241,0.001164178,0.001084808,0.003106723],"category_scores_gemma":[0.003421914,0.0006644191,0.0009584287,0.001163071,0.0005675417,0.002060235,0.001517861,0.002211101,0.001573531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006615956,"about_ca_system_score_gemma":0.0009874405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002680131,"about_ca_topic_score_gemma":0.004364404,"domain_scores_codex":[0.9992853,0.000236841,0.00004710162,0.0002358547,0.0001265455,0.00006844127],"domain_scores_gemma":[0.9988937,0.0005290575,0.00009675627,0.0002262262,0.0002192656,0.00003502515],"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":[0.0002479068,0.0003867817,0.001920876,0.0002112485,0.0003952524,0.0001645344,0.000239679,0.2903149,0.01857894,0.03039545,0.005611009,0.6515335],"study_design_scores_gemma":[0.00001516413,0.00006211344,0.0003411862,0.0000159499,0.0000300139,0.00004971705,0.00002596997,0.9778595,0.005089214,0.01452889,0.001970071,0.00001219926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01293536,0.0002908827,0.9840385,0.000154892,0.00005393926,0.00003860785,0.00009967921,0.00112723,0.001261021],"genre_scores_gemma":[0.4046017,0.0007700683,0.5813785,0.0004574799,0.0001930233,0.0003272249,0.001750326,0.0003194287,0.01020216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003106723,"threshold_uncertainty_score":0.01039296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05856459895297245,"score_gpt":0.239312822553676,"score_spread":0.1807482236007036,"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."}}