Papers with Supervised Contrastive Learning
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer Models (2023.emnlp-main)
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| Challenge: | Recent advances in NLP have led to the use of pre-trained Transformer models for transfer learning tasks becoming the most common way to solve target tasks. |
| Approach: | They propose a 3-phase technique to adjust a base model for a classification task by adapting the model’s signal to the data distribution and a new data augmentation approach for Supervised Contrastive Learning to correct the unbalanced datasets. |
| Outcome: | The proposed method is compared with other methods and compares it with other approaches. |
CEPT: A Contrast-Enhanced Prompt-Tuning Framework for Emotion Recognition in Conversation (2024.lrec-main)
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| Challenge: | Emotion recognition in conversation research suffers from data imbalance and the presence of similar linguistic expressions for different emotions. |
| Approach: | They propose a Contrast-Enhanced Prompt-Tuning framework that transforms an ERC task into a Masked Language Modeling task and generates the emotion for each utterance in the conversation. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on all three benchmark datasets and excels in recognizing minority emotions. |
An Instruction Tuning-Based Contrastive Learning Framework for Aspect Sentiment Quad Prediction with Implicit Aspects and Opinions (2024.findings-emnlp)
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| Challenge: | Existing methods for aspect-based sentiment analysis have not explored how to effectively leverage the knowledge of pre-trained language models to handle implicit aspects and opinions. |
| Approach: | They propose a framework leveraging Instruction Tuning and Supervised Contrastive Learning to improve aspect sentiment quad prediction for implicit aspects and opinions. |
| Outcome: | The proposed framework significantly outperforms existing methods on benchmark datasets. |