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    • Thrush, Tristan (6)
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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 – 7 of 7 1 Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality ... Thrush, Tristan; Jiang, Ryan; Bartolo, Max. - : arXiv, 2022 BASE Show details 2 ANLIzing the Adversarial Natural Language Inference Dataset Williams, Adina; Thrush, Tristan; Kiela, Douwe In: Proceedings of the Society for Computation in Linguistics (2022) BASE Show details 3 Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization Wilcox, Ethan; Thrush, Tristan; Levy, Roger In: Association for Computational Linguistics (2021) BASE Show details 4 Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate 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; Kiela, Douwe; Talat, Zeerak. - : Underline Science Inc., 2021 BASE Show details 5 Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bartolo, Max; Jia, Robin. - : Underline Science Inc., 2021 BASE Show details 6 Compositional Neural Machine Translation by Removing the Lexicon from Syntax ... Thrush, Tristan. - : arXiv, 2020 Abstract: The meaning of a natural language utterance is largely determined from its syntax and words. Additionally, there is evidence that humans process an utterance by separating knowledge about the lexicon from syntax knowledge. Theories from semantics and neuroscience claim that complete word meanings are not encoded in the representation of syntax. In this paper, we propose neural units that can enforce this constraint over an LSTM encoder and decoder. We demonstrate that our model achieves competitive performance across a variety of domains including semantic parsing, syntactic parsing, and English to Mandarin Chinese translation. In these cases, our model outperforms the standard LSTM encoder and decoder architecture on many or all of our metrics. To demonstrate that our model achieves the desired separation between the lexicon and syntax, we analyze its weights and explore its behavior when different neural modules are damaged. When damaged, we find that the model displays the knowledge distortions that ... : natural language processing; adversarial neural networks; machine translation; aphasia; neural attention ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2002.08899 https://dx.doi.org/10.48550/arxiv.2002.08899 BASE Hide details 7 SAL : a Self-Aware Learning system ; Self-Aware Learning system Thrush, Tristan Andrew Fraser.. - : Massachusetts Institute of Technology, 2019 BASE Show details

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