Papers with over-smoothing problem
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation (2025.coling-main)
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| Challenge: | Existing graph-based multimodal emotion recognition methods fail to capture dynamic changes in emotions. |
| Approach: | They propose a Dynamic Graph Neural Ordinary Differential Equation Network (DGODE) which combines dynamic changes of emotions to capture temporal dependencies of speakers’ emotions. |
| Outcome: | The proposed model can capture the temporal dependencies caused by dynamic changes in emotions and can improve on two publicly available multimodal emotion recognition datasets. |
Alleviating Over-smoothing for Unsupervised Sentence Representation (2023.acl-long)
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| Challenge: | Existing approaches to learn better unsupervised sentence representations have been successful . over-smoothing problem in unsupervised sentences reduces the capacity of powerful PLMs . |
| Approach: | They propose a method to solve the over-smoothing problem in unsupervised sentence representations by combining negatives from PLMs intermediate layers. |
| Outcome: | The proposed method improves on different strong baselines on Semantic Textual Similarity and Transfer datasets. |
Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent studies have used Graph Neural Networks (GNNs) to encode language knowledge into token embeddings. |
| Approach: | They propose a multi-level community-awareness Graph Neural Network layer to jointly model local and global relationships between words and their linguistic roles in multiple communities. |
| Outcome: | The proposed method reduces time complexity in very long sentences while preserving the original meaning. |
Revisiting Over-Smoothness in Text to Speech (2022.acl-long)
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| Challenge: | Non-autoregressive text to speech models ignore correlation in time and frequency domains, causing blurry results. |
| Approach: | They revisit the problem of over-smoothness in non-autoregressive text to speech models . they use methods that reduce complexity of data distributions and improve modeling methods . |
| Outcome: | The proposed models achieve better voice quality and faster inference speed than autoregressive models. |
FluentSpeech: Stutter-Oriented Automatic Speech Editing with Context-Aware Diffusion Models (2023.findings-acl)
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| Challenge: | Existing methods for speech editing still suffer from over-smoothing problem and lack of robustness due to stutter. |
| Approach: | They propose a stutter-oriented automatic speech editing model that incorporates sutter information into the hidden sequence. |
| Outcome: | The proposed model achieves state-of-the-art performance on a speech recording dataset . it can improve fluency of stuttering speech in terms of objective and subjective metrics. |