%0 Conference Proceedings %T CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment %A Senel, Lütfi Kerem %A Schick, Timo %A Schuetze, Hinrich %Y Muresan, Smaranda %Y Nakov, Preslav %Y Villavicencio, Aline %S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) %D 2022 %8 May %I Association for Computational Linguistics %C Dublin, Ireland %F senel-etal-2022-coda21 %X Pretrained language models (PLMs) have achieved superhuman performance on many benchmarks, creating a need for harder tasks. We introduce CoDA21 (Context Definition Alignment), a challenging benchmark that measures natural language understanding (NLU) capabilities of PLMs: Given a definition and a context each for k words, but not the words themselves, the task is to align the k definitions with the k contexts. CoDA21 requires a deep understanding of contexts and definitions, including complex inference and world knowledge. We find that there is a large gap between human and PLM performance, suggesting that CoDA21 measures an aspect of NLU that is not sufficiently covered in existing benchmarks. %R 10.18653/v1/2022.acl-short.92 %U https://aclanthology.org/2022.acl-short.92 %U https://doi.org/10.18653/v1/2022.acl-short.92 %P 815-824