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What it Thinks is Important is Important: Robustness Transfers through Input Gradients., , and . CoRR, (2019)The Benchmark Lottery, , , , , , , and . CoRR, (2021)Charformer: Fast Character Transformers via Gradient-based Subword Tokenization., , , , , , , , , and . CoRR, (2021)Holistic Multi-modal Memory Network for Movie Question Answering., , , , , and . CoRR, (2018)Neural architectures for natural language understanding. Nanyang Technological University, Singapore, (2019)NeuPL: Attention-based Semantic Matching and Pair-Linking for Entity Disambiguation., , , , and . CIKM, page 1667-1676. ACM, (2017)Robust Representation Learning of Biomedical Names., , and . ACL (1), page 3275-3285. Association for Computational Linguistics, (2019)On Orthogonality Constraints for Transformers., , , , , , , , and . ACL/IJCNLP (2), page 375-382. Association for Computational Linguistics, (2021)How Reliable are Model Diagnostics?, , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 1778-1785. Association for Computational Linguistics, (2021)Multi-Cast Attention Networks., , and . KDD, page 2299-2308. ACM, (2018)