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Beat the AI: Investigating Adversarial Human Annotations for Reading Comprehension., , , , and . CoRR, (2020)Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants., , , , , and . NAACL-HLT, page 3754-3767. Association for Computational Linguistics, (2022)Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets., , , and . AAAI, page 8918-8927. AAAI Press, (2020)Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets., , and . EACL, page 1000-1008. Association for Computational Linguistics, (2021)What the DAAM: Interpreting Stable Diffusion Using Cross Attention., , , , , , , , and . ACL (1), page 5644-5659. Association for Computational Linguistics, (2023)BioNLP Shared Task 2011: Supporting Resources., , , , , and . BioNLP@ACL (Shared Task), page 112-120. Association for Computational Linguistics, (2011)Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation., , , , , and . EMNLP (1), page 8830-8848. Association for Computational Linguistics, (2021)Don't Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering., , , and . EMNLP (1), page 3029-3039. Association for Computational Linguistics, (2020)Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets., , and . CoRR, (2020)PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them., , , , , , , and . Trans. Assoc. Comput. Linguistics, (2021)