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CMU-LTI at KBP 2015 Event Track., , , , and . TAC, NIST, (2015)Tricks for Training Sparse Translation Models., , , , , and . NAACL-HLT, page 3340-3345. Association for Computational Linguistics, (2022)Benefits of Intermediate Annotations in Reading Comprehension., , and . ACL, page 5627-5634. Association for Computational Linguistics, (2020)Generating Natural Adversarial Examples., , and . CoRR, (2017)To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering., , , and . CoRR, (2022)Evaluating Models' Local Decision Boundaries via Contrast Sets., , , , , , , , , and 16 other author(s). EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 1307-1323. Association for Computational Linguistics, (2020)UCI Machine Learning Repository, and . (2017)Evaluating NLP Models via Contrast Sets., , , , , , , , , and 16 other author(s). CoRR, (2020)DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs., , , , , and . NAACL-HLT (1), page 2368-2378. Association for Computational Linguistics, (2019)Comprehensive Multi-Dataset Evaluation of Reading Comprehension., , , , and . MRQA@EMNLP, page 147-153. Association for Computational Linguistics, (2019)