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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 Page: 1 2 3 Hits 1 – 20 of 41 1 Neural reality of argument structure constructions ... Li, Bai; Zhu, Zining; Thomas, Guillaume. - : arXiv, 2022 BASE Show details 2 On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems ... Berlot-Attwell, Ian; Rudzicz, Frank. - : arXiv, 2021 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 ... Zhu, Zining; Balagopalan, Aparna; Ghassemi, Marzyeh. - : arXiv, 2021 BASE Show details 5 An {E}valuation of {D}isentangled {R}epresentation {L}earning for {T}exts ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Hirst, Graeme; Rudzicz, Frank; Vishnubhotla, Krishnapriya. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.170 Abstract: Learning disentangled representations of texts, which encode information pertaining to different aspects in separate representations, is an active area of research in NLP for controllable and interpretable text generation. These methods have, for the most part, been developed in the context of text style transfer, but are limited in their evaluation. In this work, we look at the motivation behind learning disentangled representations of content and style for texts and at the potential use-cases when compared to end-to-end methods, and we propose evaluation metrics that correspond to these use-cases. We further conduct a systematic investigation of previously proposed loss functions for such models and evaluate them on a highly-structured and synthetic natural language dataset which is well-suited for the task of disentangled representation learning, as well as two other parallel style transfer datasets. Our results demonstrate that ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/26261-an-evaluation-of-disentangled-representation-learning-for-texts https://dx.doi.org/10.48448/265d-yd97 BASE Hide details 6 How is BERT surprised? Layerwise detection of linguistic anomalies ... Li, Bai; Zhu, Zining; Thomas, Guillaume. - : arXiv, 2021 BASE Show details 7 Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech Balagopalan, Aparna; Eyre, Benjamin; Robin, Jessica... In: Front Aging Neurosci (2021) BASE Show details 8 Identification of primary and collateral tracks in stuttered speech Riad, Rachid; Bachoud-Lévi, Anne-Catherine; Rudzicz, Frank... 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) BASE Show details 9 Semantic coordinates analysis reveals language changes in the AI field ... Zhu, Zining; Xu, Yang; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 10 Word class flexibility: A deep contextualized approach ... Li, Bai; Thomas, Guillaume; Xu, Yang. - : arXiv, 2020 BASE Show details 11 To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection ... Balagopalan, Aparna; Eyre, Benjamin; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 12 An information theoretic view on selecting linguistic probes ... Zhu, Zining; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 13 Examining the rhetorical capacities of neural language models ... Zhu, Zining; Pan, Chuer; Abdalla, Mohamed. - : arXiv, 2020 BASE Show details 14 A textual analysis of US corporate social responsibility reports Clarkson, Peter M.; Ponn, Jordan; Richardson, Gordon D.. - : Wiley-Blackwell, 2020 BASE Show details 15 Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power ... Novikova, Jekaterina; Balagopalan, Aparna; Shkaruta, Ksenia. - : arXiv, 2019 BASE Show details 16 Representation Learning for Discovering Phonemic Tone Contours ... Li, Bai; Xie, Jing Yi; Rudzicz, Frank. - : arXiv, 2019 BASE Show details 17 Machine learning for MEG during speech tasks Kostas, Demetres; Pang, Elizabeth W.; Rudzicz, Frank. - : Nature Publishing Group UK, 2019 BASE Show details 18 The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech ... Balagopalan, Aparna; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details 19 Detecting cognitive impairments by agreeing on interpretations of linguistic features ... Zhu, Zining; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details 20 Deconfounding age effects with fair representation learning when assessing dementia ... Zhu, Zining; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details Page: 1 2 3

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