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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
An {E}valuation of {D}isentangled {R}epresentation {L}earning for {T}exts ...
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6
How is BERT surprised? Layerwise detection of linguistic anomalies ...
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7
Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech
In: Front Aging Neurosci (2021)
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8
Identification of primary and collateral tracks in stuttered speech
In: LREC 2020 - 12th Conference on Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-02959454 ; LREC 2020 - 12th Conference on Language Resources and Evaluation, May 2020, Marseille, France (2020)
Abstract: International audience ; Disfluent speech has been previously addressed from two main perspectives: the clinical perspective focusing on diagnostic, and the Natural Language Processing (NLP) perspective aiming at modeling these events and detect them for downstream tasks. In addition, previous works often used different metrics depending on whether the input features are text or speech, making it difficult to compare the different contributions. Here, we introduce a new evaluation framework for disfluency detection inspired by the clinical and NLP perspective together with the theory of performance from (Clark, 1996) which distinguishes between primary and collateral tracks. We introduce a novel forced-aligned disfluency dataset from a corpus of semi-directed interviews, and present baseline results directly comparing the performance of text-based features (word and span information) and speech-based (acoustic-prosodic information). Finally, we introduce new audio features inspired by the word-based span features. We show experimentally that using these features outperformed the baselines for speech-based predictions on the present dataset.
Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; Audio features; Disfluency; Evaluation metrics; Speech processing; Stuttering
URL: https://hal.archives-ouvertes.fr/hal-02959454/document
https://hal.archives-ouvertes.fr/hal-02959454
https://hal.archives-ouvertes.fr/hal-02959454/file/2003.01018.pdf
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9
Semantic coordinates analysis reveals language changes in the AI field ...
Zhu, Zining; Xu, Yang; Rudzicz, Frank. - : arXiv, 2020
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10
Word class flexibility: A deep contextualized approach ...
Li, Bai; Thomas, Guillaume; Xu, Yang. - : arXiv, 2020
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11
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection ...
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12
An information theoretic view on selecting linguistic probes ...
Zhu, Zining; Rudzicz, Frank. - : arXiv, 2020
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13
Examining the rhetorical capacities of neural language models ...
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14
A textual analysis of US corporate social responsibility reports
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15
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power ...
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16
Representation Learning for Discovering Phonemic Tone Contours ...
Li, Bai; Xie, Jing Yi; Rudzicz, Frank. - : arXiv, 2019
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17
Machine learning for MEG during speech tasks
Kostas, Demetres; Pang, Elizabeth W.; Rudzicz, Frank. - : Nature Publishing Group UK, 2019
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18
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech ...
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19
Detecting cognitive impairments by agreeing on interpretations of linguistic features ...
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20
Deconfounding age effects with fair representation learning when assessing dementia ...
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