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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 – 13 of 13 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 RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Lee, Dong-Ho; Lin, Yuchen. - : Underline Science Inc., 2021 BASE Show details 3 Learning Contextualized Knowledge Structures for Commonsense Reasoning ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chan, Aaron; Kim , Sungchul. - : Underline Science Inc., 2021 BASE Show details 4 AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Dong, lunadong@amazon.com; Grant, Christan. - : Underline Science Inc., 2021 BASE Show details 5 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 6 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. - : Underline Science Inc., 2021 BASE Show details 7 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 8 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 9 Improving Counterfactual Generation for Fair Hate Speech Detection ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Atari, Mohammad; Dehghani, Morteza. - : Underline Science Inc., 2021 BASE Show details 10 Learning to Generate Task-Specific Adapters from Task Description ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Ren, Xiang; Ye, Qinyuan. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.82 Abstract: Pre-trained text-to-text transformers such as BART have achieved impressive performance across a range of NLP tasks. Recent study further shows that they can learn to generalize to novel tasks, by including task descriptions as part of the source sequence and training the model with (source, target) examples. At test time, these fine-tuned models can make inferences on new tasks using the new task descriptions as part of the input. However, this approach has potential limitations, as the model learns to solve individual (source, target) examples (i.e., at the instance level), instead of learning to solve tasks by taking all examples within a task as a whole (i.e., at the task level). To this end, we introduce Hypter, a framework that improves text-to-text transformer's generalization ability to unseen tasks by training a hypernetwork to generate task-specific, light-weight adapters from task descriptions. Experiments on ZEST dataset and a ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/26044-learning-to-generate-task-specific-adapters-from-task-description https://dx.doi.org/10.48448/08rn-0j25 BASE Hide details 11 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 12 Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Lee, Seyeon; Lin, Yuchen. - : Underline Science Inc., 2021 BASE Show details 13 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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