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Manipulating the Difficulty of C-Tests.

, , and . ACL (1), page 360-370. Association for Computational Linguistics, (2019)

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Lessons Learned from a Citizen Science Project for Natural Language Processing., , , , , , , , and . EACL, page 3576-3590. Association for Computational Linguistics, (2023)TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation., , , , , , and . AACL/IJCNLP (System Demonstrations), page 9-16. Association for Computational Linguistics, (2022)Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems., , , , , , , , and . NAACL-HLT (1), page 1634-1647. Association for Computational Linguistics, (2019)Empowering Active Learning to Jointly Optimize System and User Demands., , and . ACL, page 4233-4247. Association for Computational Linguistics, (2020)Constrained C-Test Generation via Mixed-Integer Programming., , and . CoRR, (2024)Annotation Curricula to Implicitly Train Non-Expert Annotators., , and . CoRR, (2021)Transformers with Learnable Activation Functions., , , and . CoRR, (2022)Erratum: Annotation Curricula to Implicitly Train Non-Expert Annotators., , and . Comput. Linguistics, 48 (4): 1141 (2022)Reconfigurable multi-function logic based on graphene P-N junctions., , , and . DAC, page 883-888. ACM, (2010)Efficient Methods for Natural Language Processing: A Survey., , , , , , , , , and 8 other author(s). CoRR, (2022)