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MAGIC DUST FOR CROSS-LINGUAL ADAPTATION OF MONOLINGUAL WAV2VEC-2.0
In: ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03544515 ; ICASSP 2022, May 2022, Singapour, Singapore (2022)
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Simple and Effective Unsupervised Speech Synthesis ...
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Learning Audio-Video Language Representations
Rouditchenko, Andrew. - : Massachusetts Institute of Technology, 2021
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4
Cascaded Multilingual Audio-Visual Learning from Videos ...
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Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0 ...
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Text-Free Image-to-Speech Synthesis Using Learned Segmental Units ...
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7
Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.415/ Abstract: Exposure bias has been regarded as a central problem for auto-regressive language models (LM). It claims that teacher forcing would cause the test-time generation to be incrementally distorted due to the training-generation discrepancy. Although a lot of algorithms have been proposed to avoid teacher forcing and therefore alleviate exposure bias, there is little work showing how serious the exposure bias problem actually is. In this work, we focus on the task of open-ended language generation, propose metrics to quantify the impact of exposure bias in the aspects of quality, diversity, and consistency. Our key intuition is that if we feed ground-truth data prefixes (instead of prefixes generated by the model itself) into the model and ask it to continue the generation, the performance should become much better because the training-generation discrepancy in the prefix is removed. Both automatic and human evaluations are conducted in ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Generation
URL: https://dx.doi.org/10.48448/es4n-qm98
https://underline.io/lecture/37561-exposure-bias-versus-self-recovery-are-distortions-really-incremental-for-autoregressive-text-generationquestion
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8
Mitigating Biases in Toxic Language Detection through Invariant Rationalization ...
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Mitigating Biases in Toxic Language Detection through Invariant Rationalization ...
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10
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning
In: Interspeech 2020 ; https://hal.archives-ouvertes.fr/hal-02912029 ; Interspeech 2020, Oct 2020, Shanghai, China (2020)
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11
Similarity Analysis of Contextual Word Representation Models ...
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CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning ...
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13
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning ...
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14
What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context ...
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15
Vector-Quantized Autoregressive Predictive Coding ...
Chung, Yu-An; Tang, Hao; Glass, James. - : arXiv, 2020
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16
Non-Autoregressive Predictive Coding for Learning Speech Representations from Local Dependencies ...
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17
Improved Speech Representations with Multi-Target Autoregressive Predictive Coding ...
Chung, Yu-An; Glass, James. - : arXiv, 2020
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18
Classifying Alzheimer's Disease Using Audio and Text-Based Representations of Speech
In: Frontiers (2020)
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19
Identification of digital voice biomarkers for cognitive health
In: Explor Med (2020)
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20
On the Linguistic Representational Power of Neural Machine Translation Models
In: Computational Linguistics, Vol 46, Iss 1, Pp 1-52 (2020) (2020)
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