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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 – 10 of 10 1 Emotion Intensity and its Control for Emotional Voice Conversion ... Zhou, Kun; Sisman, Berrak; Rana, Rajib. - : arXiv, 2022 BASE Show details 2 Limited Data Emotional Voice Conversion Leveraging Text-to-Speech: Two-stage Sequence-to-Sequence Training ... Zhou, Kun; Sisman, Berrak; Li, Haizhou. - : arXiv, 2021 BASE Show details 3 Identity Conversion for Emotional Speakers: A Study for Disentanglement of Emotion Style and Speaker Identity ... Du, Zongyang; Sisman, Berrak; Zhou, Kun. - : arXiv, 2021 BASE Show details 4 CRSLab: An Open-Source Toolkit for Building Conversational Recommender System ... 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; Cheng, Yuan; Li, Yaliang. - : Underline Science Inc., 2021 BASE Show details 5 Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Zhou, Kun. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.315/ Abstract: Recent works have shown that powerful pretrained language models (PLM) can be fooled by small perturbations or intentional attacks. To solve this issue, various data augmentation techniques are proposed to improve the robustness of PLMs. However, it is still challenging to augment semantically relevant examples with sufficient diversity. In this work, we present Virtual Data Augmentation (VDA), a general framework for robustly fine-tuning PLMs. Based on the original token embeddings, we construct a multinomial mixture for augmenting virtual data embeddings, where a masked language model guarantees the semantic relevance and the Gaussian noise provides the augmentation diversity. Furthermore, a regularized training strategy is proposed to balance the two aspects. Extensive experiments on six datasets show that our approach is able to improve the robustness of PLMs and alleviate the performance degradation under adversarial attacks. ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://dx.doi.org/10.48448/aa28-e453 https://underline.io/lecture/38065-virtual-data-augmentation-a-robust-and-general-framework-for-fine-tuning-pre-trained-models BASE Hide details 6 VAW-GAN for Disentanglement and Recomposition of Emotional Elements in Speech ... Zhou, Kun; Sisman, Berrak; Li, Haizhou. - : arXiv, 2020 BASE Show details 7 Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset ... Zhou, Kun; Sisman, Berrak; Liu, Rui. - : arXiv, 2020 BASE Show details 8 Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion ... Zhou, Kun; Sisman, Berrak; Zhang, Mingyang. - : arXiv, 2020 BASE Show details 9 Transforming Spectrum and Prosody for Emotional Voice Conversion with Non-Parallel Training Data ... Zhou, Kun; Sisman, Berrak; Li, Haizhou. - : arXiv, 2020 BASE Show details 10 VAW-GAN for Singing Voice Conversion with Non-parallel Training Data ... Lu, Junchen; Zhou, Kun; Sisman, Berrak. - : arXiv, 2020 BASE Show details

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