{"id":"W4391760761","doi":"10.1145/3632754.3633076","title":"CIRAL at FIRE 2023: Cross-Lingual Information Retrieval for African Languages","year":2023,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Universitas Brawijaya","keywords":"Yoruba; Swahili; Computer science; Hausa; Relevance (law); Natural language processing; Artificial intelligence; Information retrieval; Languages of Africa; Task (project management); Linguistics; Political science","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":[0.02106003,0.002439802,0.00237545,0.006655069,0.004497541,0.004225083,0.003189127,0.00266009,0.02507153],"category_scores_gemma":[0.0182618,0.0007031467,0.001782318,0.004315591,0.00119054,0.007592773,0.006651517,0.003945026,0.01687821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002387792,"about_ca_system_score_gemma":0.004297773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02359932,"about_ca_topic_score_gemma":0.0299506,"domain_scores_codex":[0.9897377,0.004189824,0.0004749612,0.001169178,0.00292372,0.001504597],"domain_scores_gemma":[0.9877401,0.002866715,0.0003438075,0.002424761,0.004736192,0.001888438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00273255,0.002225004,0.002981691,0.001733213,0.0002621936,0.0003901734,0.00112153,0.00138022,0.02728253,0.003665735,0.6996168,0.2566084],"study_design_scores_gemma":[0.002717111,0.003972124,0.03926391,0.0007746259,0.0004093999,0.001724257,0.003651319,0.02654765,0.06402668,0.005976208,0.8502991,0.0006376358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.285072,0.02319483,0.1290501,0.01132778,0.01183256,0.02225634,0.3267412,0.08559676,0.1049284],"genre_scores_gemma":[0.1675294,0.002445942,0.2048123,0.002941491,0.001262209,0.00921316,0.5489761,0.006715325,0.05610403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02507153,"threshold_uncertainty_score":0.1113774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01495960381941224,"score_gpt":0.3202101600306263,"score_spread":0.3052505562112141,"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."}}