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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. - : arXiv, 2021 BASE Show 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; Rudzicz, Frank. - : arXiv, 2020 Abstract: Recently, neural language models (LMs) have demonstrated impressive abilities in generating high-quality discourse. While many recent papers have analyzed the syntactic aspects encoded in LMs, there has been no analysis to date of the inter-sentential, rhetorical knowledge. In this paper, we propose a method that quantitatively evaluates the rhetorical capacities of neural LMs. We examine the capacities of neural LMs understanding the rhetoric of discourse by evaluating their abilities to encode a set of linguistic features derived from Rhetorical Structure Theory (RST). Our experiments show that BERT-based LMs outperform other Transformer LMs, revealing the richer discourse knowledge in their intermediate layer representations. In addition, GPT-2 and XLNet apparently encode less rhetorical knowledge, and we suggest an explanation drawing from linguistic philosophy. Our method shows an avenue towards quantifying the rhetorical capacities of neural LMs. ... : EMNLP 2020 BlackboxNLP Workshop ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2010.00153 https://dx.doi.org/10.48550/arxiv.2010.00153 BASE Hide 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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