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Hits 1 – 16 of 16

1
Neural reality of argument structure constructions ...
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2
On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems ...
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3
TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction ...
Li, Bai; Rudzicz, Frank. - : arXiv, 2021
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4
Quantifying the Task-Specific Information in Text-Based Classifications ...
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5
How is BERT surprised? Layerwise detection of linguistic anomalies ...
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6
Semantic coordinates analysis reveals language changes in the AI field ...
Zhu, Zining; Xu, Yang; Rudzicz, Frank. - : arXiv, 2020
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7
Word class flexibility: A deep contextualized approach ...
Li, Bai; Thomas, Guillaume; Xu, Yang. - : arXiv, 2020
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8
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection ...
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9
An information theoretic view on selecting linguistic probes ...
Zhu, Zining; Rudzicz, Frank. - : arXiv, 2020
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10
Examining the rhetorical capacities of neural language models ...
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
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11
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power ...
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12
Representation Learning for Discovering Phonemic Tone Contours ...
Li, Bai; Xie, Jing Yi; Rudzicz, Frank. - : arXiv, 2019
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13
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech ...
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14
Detecting cognitive impairments by agreeing on interpretations of linguistic features ...
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15
Dropout during inference as a model for neurological degeneration in an image captioning network ...
Li, Bai; Zhang, Ran; Rudzicz, Frank. - : arXiv, 2018
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16
Predicting health inspection results from online restaurant reviews ...
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