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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gheini, Mozhdeh; May, Jonathan. - : Underline Science Inc., 2021 BASE Show details 2 RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ho, Daniel; Khanna, Rahul. - : Underline Science Inc., 2021 BASE Show details 3 Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Jin, Xisen; Lin, Yuchen. - : Underline Science Inc., 2021 BASE Show details 4 RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gao, Wenyang; Lin, Yuchen; Moreno, Ryan; Ren, Xiang; Yan, Jun. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.302/ Abstract: To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples. Specifically, at the entity level, we replace target entities with other entities of the same semantic class in Wikidata; at the context level, we use pre-trained language models (e.g., BERT) to generate word substitutions. Together, the two levels of at- tack produce natural adversarial examples that result in a shifted distribution from the training data on which our target models have been trained. We apply the proposed method to the OntoNotes dataset and create a new benchmark named OntoRock for evaluating the robustness of existing NER models via a systematic evaluation protocol. Our experiments and analysis reveal that even the best model has a significant performance drop, and these models seem to memorize in-domain entity patterns instead of reasoning from the context. ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Named Entity Recognition; Natural Language Processing URL: https://underline.io/lecture/37488-rockner-a-simple-method-to-create-adversarial-examples-for-evaluating-the-robustness-of-named-entity-recognition-models https://dx.doi.org/10.48448/23e5-7203 BASE Hide details 5 ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Han, Rujun; Peng, Nanyun. - : Underline Science Inc., 2021 BASE Show details 6 Discretized Integrated Gradients for Explaining Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ren, Xiang; Sanyal, Soumya. - : Underline Science Inc., 2021 BASE Show details 7 Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Galstyan, Aram; Mehrabi, Ninareh. - : Underline Science Inc., 2021 BASE Show details 8 Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Han, Jiawei; Mao, Yuning. - : Underline Science Inc., 2021 BASE Show details

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