{"id":"W4221165901","doi":"10.18653/v1/2022.acl-long.265","title":"Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","topic":"ICT in Developing Communities","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Theme (computing); Computer science; Languages of Africa; Diversity (politics); Resource (disambiguation); Linguistic diversity; Linguistics; Artificial intelligence; Sociology; World Wide Web; Anthropology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02457809,0.0006329921,0.0005918691,0.003854276,0.009123879,0.01958703,0.001670548,0.004478269,0.0139314],"category_scores_gemma":[0.02800094,0.0006647159,0.0005025067,0.004785705,0.008207314,0.03226771,0.01305982,0.005751029,0.00467059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003143088,"about_ca_system_score_gemma":0.0103919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005227996,"about_ca_topic_score_gemma":0.007520309,"domain_scores_codex":[0.9902796,0.006407254,0.0005631102,0.0006775995,0.001054315,0.001018104],"domain_scores_gemma":[0.9827071,0.009067947,0.001149078,0.00200837,0.003915191,0.001152256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007865337,0.00008728239,0.003682894,0.000802608,0.00001871928,0.0007276815,0.0297804,0.0009166647,0.004225599,0.7097282,0.04071078,0.2092405],"study_design_scores_gemma":[0.00001940239,0.00003054632,0.001748537,0.001389051,0.00001983584,0.0006765081,0.03339507,0.003236273,0.004245431,0.3104751,0.6446966,0.00006770777],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0476633,0.01276208,0.4109587,0.3494924,0.002627942,0.0005677131,0.001416649,0.00142863,0.1730825],"genre_scores_gemma":[0.3852742,0.01568493,0.5278543,0.01953984,0.001556886,0.001010332,0.003189782,0.001265998,0.04462369],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02457809,"threshold_uncertainty_score":0.1299829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0106674767763445,"score_gpt":0.2349688958012145,"score_spread":0.22430141902487,"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."}}