DE eng

Search in the Catalogues and Directories

Hits 1 – 16 of 16

1
Neural reality of argument structure constructions ...
BASE
Show details
2
On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems ...
BASE
Show details
3
TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction ...
Li, Bai; Rudzicz, Frank. - : arXiv, 2021
BASE
Show details
4
Quantifying the Task-Specific Information in Text-Based Classifications ...
BASE
Show details
5
How is BERT surprised? Layerwise detection of linguistic anomalies ...
BASE
Show details
6
Semantic coordinates analysis reveals language changes in the AI field ...
Zhu, Zining; Xu, Yang; Rudzicz, Frank. - : arXiv, 2020
BASE
Show details
7
Word class flexibility: A deep contextualized approach ...
Li, Bai; Thomas, Guillaume; Xu, Yang. - : arXiv, 2020
BASE
Show details
8
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection ...
BASE
Show details
9
An information theoretic view on selecting linguistic probes ...
Zhu, Zining; Rudzicz, Frank. - : arXiv, 2020
BASE
Show details
10
Examining the rhetorical capacities of neural language models ...
BASE
Show details
11
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power ...
BASE
Show details
12
Representation Learning for Discovering Phonemic Tone Contours ...
Li, Bai; Xie, Jing Yi; Rudzicz, Frank. - : arXiv, 2019
BASE
Show details
13
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech ...
Abstract: Speech datasets for identifying Alzheimer's disease (AD) are generally restricted to participants performing a single task, e.g. describing an image shown to them. As a result, models trained on linguistic features derived from such datasets may not be generalizable across tasks. Building on prior work demonstrating that same-task data of healthy participants helps improve AD detection on a single-task dataset of pathological speech, we augment an AD-specific dataset consisting of subjects describing a picture with multi-task healthy data. We demonstrate that normative data from multiple speech-based tasks helps improve AD detection by up to 9%. Visualization of decision boundaries reveals that models trained on a combination of structured picture descriptions and unstructured conversational speech have the least out-of-task error and show the most potential to generalize to multiple tasks. We analyze the impact of age of the added samples and if they affect fairness in classification. We also provide ... : Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216 ...
Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Machine Learning stat.ML; Sound cs.SD
URL: https://dx.doi.org/10.48550/arxiv.1811.12254
https://arxiv.org/abs/1811.12254
BASE
Hide details
14
Detecting cognitive impairments by agreeing on interpretations of linguistic features ...
BASE
Show details
15
Dropout during inference as a model for neurological degeneration in an image captioning network ...
Li, Bai; Zhang, Ran; Rudzicz, Frank. - : arXiv, 2018
BASE
Show details
16
Predicting health inspection results from online restaurant reviews ...
BASE
Show details

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
16
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern