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Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study.

, , , , и . AfricaNLP, (2020)

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Verena Luger

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Max Luger

 

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Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara., , , , , , , , и . CoRR, (2020)Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning., , , , и . ACL (1), стр. 950-966. Association for Computational Linguistics, (2023)Health Care Misinformation: an Artificial Intelligence Challenge for Low-resource languages., , , , , и . AI4SG@AAAI Fall Symposium, том 2884 из CEUR Workshop Proceedings, CEUR-WS.org, (2020)Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study., , , , и . AfricaNLP, (2020)Type nanotheories: a framework for term comparison., , и . CIKM, стр. 701-710. ACM, (2007)Offensiveness as an Opinion: Dissecting population-level Label Distributions., , , и . Tiny Papers @ ICLR, OpenReview.net, (2023)Algorithms for assessing the quality and difficulty of multiple choice exam questions.. University of Edinburgh, UK, (2016)British Library, EThOS.A Graph Theory Approach for Generating Multiple Choice Exams.. AAAI Fall Symposium: Question Generation, том FS-11-04 из AAAI Technical Report, AAAI, (2011)Comparative Methods and Analysis for Creating High-Quality Question Sets from Crowdsourced Data., и . FLAIRS, стр. 185-190. AAAI Press, (2016)Two Methods for Measuring Question Difficulty and Discrimination in Incomplete Crowdsourced Data., и . HCOMP (Works in Progress / Demos), том WS-13-18 из AAAI Technical Report, AAAI, (2013)