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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 Page: 1 2 Hits 1 – 20 of 31 1 Natural Language Descriptions of Deep Visual Features ... Hernandez, Evan; Schwettmann, Sarah; Bau, David. - : arXiv, 2022 BASE Show details 2 Compositionality as Lexical Symmetry ... Akyürek, Ekin; Andreas, Jacob. - : arXiv, 2022 BASE Show details 3 Language as a bootstrap for compositional visual reasoning Wong, Catherine; Friedman, Yoni; Andreas, Jacob... In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 4 Compositional Models for Few Shot Sequence Learning Akyurek, Ekin. - : Massachusetts Institute of Technology, 2021 BASE Show details 5 Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales ... Andreas, Jacob; Beguš, Gašper; Bronstein, Michael M.. - : arXiv, 2021 BASE Show details 6 Implicit Representations of Meaning in Neural Language Models ... Li, Belinda Z.; Nye, Maxwell; Andreas, Jacob. - : arXiv, 2021 BASE Show details 7 Implicit Representations of Meaning in Neural Language Models ... 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; Andreas, Jacob; Li, Belinda. - : Underline Science Inc., 2021 BASE Show details 8 How Do Neural Sequence Models Generalize? Local and Global Cues for Out-of-Distribution Prediction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Andreas, Jacob; Bau, Anthony. - : Underline Science Inc., 2021 BASE Show details 9 Value-Agnostic Conversational Semantic Parsing ... 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; Andreas, Jacob; Guo, algu@microsoft.com. - : Underline Science Inc., 2021 BASE Show details 10 Lexicon Learning for Few-Shot Neural Sequence Modeling ... Akyürek, Ekin; Andreas, Jacob. - : arXiv, 2021 BASE Show details 11 What Context Features Can Transformer Language Models Use? ... O'Connor, Joe; Andreas, Jacob. - : arXiv, 2021 BASE Show details 12 Quantifying Adaptability in Pre-trained Language Models with 500 Tasks ... Li, Belinda Z.; Yu, Jane; Khabsa, Madian. - : arXiv, 2021 BASE Show details 13 One-Shot Lexicon Learning for Low-Resource Machine Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Andreas, Jacob; Kantharuban, Anjali. - : Underline Science Inc., 2021 BASE Show details 14 Lexicon Learning for Few Shot Sequence Modeling ... 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; Akyürek, Ekin; Andreas, Jacob. - : Underline Science Inc., 2021 BASE Show details 15 What Context Features Can Transformer Language Models Use? ... 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; Andreas, Jacob; O'Connor, Joe. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.70 Abstract: Transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. What aspects of these contexts contribute to accurate model prediction? We describe a series of experiments that measure usable information by selectively ablating lexical and structural information in transformer language models trained on English Wikipedia. In both mid- and long-range contexts, we find that several extremely destructive context manipulations---including shuffling word order within sentences and deleting all words other than nouns---remove less than 15% of the usable information. Our results suggest that long contexts, but not their detailed syntactic and propositional content, are important for the low perplexity of current transformer language models. ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/sqvk-y475 https://underline.io/lecture/25433-what-context-features-can-transformer-language-models-usequestion BASE Hide details 16 The Low-Dimensional Linear Geometry of Contextualized Word Representations ... Hernandez, Evan; Andreas, Jacob. - : arXiv, 2021 BASE Show details 17 The Low-Dimensional Linear Geometry of Contextualized Word Representations ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Andreas, Jacob; Hernandez, Evan. - : Underline Science Inc., 2021 BASE Show details 18 Experience Grounds Language ... Bisk, Yonatan; Holtzman, Ari; Thomason, Jesse. - : arXiv, 2020 BASE Show details 19 Compositional Explanations of Neurons ... Mu, Jesse; Andreas, Jacob. - : arXiv, 2020 BASE Show details 20 A Benchmark for Systematic Generalization in Grounded Language Understanding ... Ruis, Laura; Andreas, Jacob; Baroni, Marco. - : arXiv, 2020 BASE Show details Page: 1 2

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