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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 Neural reality of argument structure constructions ... Li, Bai; Zhu, Zining; Thomas, Guillaume. - : arXiv, 2022 BASE Show details 2 Quantifying the Task-Specific Information in Text-Based Classifications ... Zhu, Zining; Balagopalan, Aparna; Ghassemi, Marzyeh; Rudzicz, Frank. - : arXiv, 2021 Abstract: Recently, neural natural language models have attained state-of-the-art performance on a wide variety of tasks, but the high performance can result from superficial, surface-level cues (Bender and Koller, 2020; Niven and Kao, 2020). These surface cues, as the ``shortcuts'' inherent in the datasets, do not contribute to the *task-specific information* (TSI) of the classification tasks. While it is essential to look at the model performance, it is also important to understand the datasets. In this paper, we consider this question: Apart from the information introduced by the shortcut features, how much task-specific information is required to classify a dataset? We formulate this quantity in an information-theoretic framework. While this quantity is hard to compute, we approximate it with a fast and stable method. TSI quantifies the amount of linguistic knowledge modulo a set of predefined shortcuts -- that contributes to classifying a sample from each dataset. This framework allows us to compare across ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2110.08931 https://arxiv.org/abs/2110.08931 BASE Hide details 3 How is BERT surprised? Layerwise detection of linguistic anomalies ... Li, Bai; Zhu, Zining; Thomas, Guillaume. - : arXiv, 2021 BASE Show details 4 Semantic coordinates analysis reveals language changes in the AI field ... Zhu, Zining; Xu, Yang; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 5 An information theoretic view on selecting linguistic probes ... Zhu, Zining; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 6 Examining the rhetorical capacities of neural language models ... Zhu, Zining; Pan, Chuer; Abdalla, Mohamed. - : arXiv, 2020 BASE Show details 7 Detecting cognitive impairments by agreeing on interpretations of linguistic features ... Zhu, Zining; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details 8 Deconfounding age effects with fair representation learning when assessing dementia ... Zhu, Zining; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details

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