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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; Thrush, Tristan; Vidgen, Bertie. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.132 Abstract: We present a human-and-model-in-the-loop process for dynamically generating datasets and training better performing and more robust hate detection models. We provide a new dataset of 40,000 entries, generated and labelled by trained annotators over four rounds of dynamic data creation. It includes 15,000 challenging perturbations and each hateful entry has fine-grained labels for the type and target of hate. Hateful entries make up 54% of the dataset, which is substantially higher than comparable datasets. We show that model performance is substantially improved using this approach. Models trained on later rounds of data collection perform better on test sets and are harder for annotators to trick. They also have better performance on HateCheck, a suite of functional tests for online hate detection. We provide the code, dataset and annotation guidelines for other researchers to use. ... 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/znsf-s209 https://underline.io/lecture/25441-learning-from-the-worst-dynamically-generated-datasets-to-improve-online-hate-detection BASE Hide 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 BASE Show 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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