Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing contrastive methods for learning universal sentence embeddings have limitations due to their over-parameterization and poor performance under domain shift settings. |
| Approach: | They propose to integrate an Energy-based Hinge loss to enhance the pairwise discriminative power of contrastive learning for sentence embeddings by combining PLMs with energy-based learning. |
| Outcome: | Empirical results show that the proposed method improves on seven standard semantic textual similarity tasks and a domain-shifted STS task. |
Similar Papers
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for learning universal sentence embeddings are based on unsupervised approaches with only dropout as noise. |
| Approach: | They propose an unsupervised approach that takes an input sentence and predicts itself in a contrastive objective with only standard dropout used as noise. |
| Outcome: | The proposed framework performs on par with previous supervised approaches and can produce superior sentence embeddings from unlabeled or labeled data. |
English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings (2022.emnlp-main)
Copied to clipboard
| Challenge: | mSimCSE can learn high-quality universal cross-lingual sentence embeddings without any parallel data. |
| Approach: | They propose a new language-based sentence embedding system that extends SimCSE to multilingual settings. |
| Outcome: | The proposed method improves existing methods on retrieval and multilingual STS tasks. |
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. |
| Approach: | They propose a novel Contrastive learning method with Prompt-derived Virtual semantic prototypes that constructs virtual semantic prototype to each instance and derives negative prototypes by using the negative form of the prompts. |
| Outcome: | The proposed method performs on semantic textual similarity, transfer, and clustering tasks compared to baselines. |
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering (2025.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on prompt engineering to encode the full semantics of a sentence into the embedding of the last token. |
| Approach: | They propose a technique that introduces an extra auxiliary prompt to elicit better sentence embedding . they propose to use the hidden state of the token as the sentence embedded in LLMs . |
| Outcome: | The proposed technique can improve performance of existing prompt-based methods on STS tasks and downstream classification tasks. |
RobustEmbed: Robust Sentence Embeddings Using Self-Supervised Contrastive Pre-Training (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing PLMs suffer from poor robustness in adversarial scenarios, despite their success with unseen samples. |
| Approach: | They propose a self-supervised sentence embedding framework that enhances generalization and robustness in various text representation tasks and against diverse adversarial attacks. |
| Outcome: | The proposed framework improves generalization and robustness in various representation tasks and against diverse adversarial attacks. |
WhitenedCSE: Whitening-based Contrastive Learning of Sentence Embeddings (2023.acl-long)
Copied to clipboard
| Challenge: | Extensive experiments on seven semantic textual similarity tasks show our method achieves consistent improvement over the contrastive learning baseline and sets new states of the art. |
| Approach: | They propose a whitening-based contrastive learning method for sentence embedding learning which combines contrastive and shuffled group whitening. |
| Outcome: | The proposed method achieves better alignment and uniformity on seven semantic textual similarity tasks. |
Improving Contrastive Learning of Sentence Embeddings with Focal InfoNCE (2023.findings-emnlp)
Copied to clipboard
| Challenge: | SimCSE does not fully exploit the potential of hard negative samples in contrastive learning. |
| Approach: | They propose an unsupervised contrastive learning framework that combines SimCSE with hard negative mining to enhance the quality of sentence embeddings. |
| Outcome: | The proposed framework improves sentence embeddings on various STS benchmarks in terms of Spearman’s correlation, representation alignment and uniformity. |
OssCSE: Overcoming Surface Structure Bias in Contrastive Learning for Unsupervised Sentence Embedding (2023.emnlp-main)
Copied to clipboard
Zhan Shi, Guoyin Wang, Ke Bai, Jiwei Li, Xiang Li, Qingjun Cui, Belinda Zeng, Trishul Chilimbi, Xiaodan Zhu
| Challenge: | Recent studies show that contrastive learning is effective in sentence representation learning . but, the surface structure bias is a problem in the current model . |
| Approach: | They propose to combine a sentence with a sub-semantic sentence to investigate the surface structure bias. |
| Outcome: | The proposed model achieves state-of-the-art on standard semantic textual similarity tasks using different pre-trained backbones. |
DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
Copied to clipboard
| Challenge: | Conventional approaches to learning sentence embeddings from dialogues employ the siamese-network for this task, but such architecture yields a large gap between training and evaluating. |
| Approach: | They propose a dialogue-based contrastive learning approach to learn sentence embeddings from dialogues using a siamese-network. |
| Outcome: | The proposed model outperforms baseline methods on three multi-turn dialogue datasets in terms of MAP and Spearman’s correlation measures. |
AugCSE: Contrastive Sentence Embedding with Diverse Augmentations (2022.aacl-main)
Copied to clipboard
| Challenge: | Similar work has shown that a single augmentation can be used to learn a robust generalpurpose representation with contrastive learning. |
| Approach: | They propose a unified framework to utilize diverse sets of data augmentations to achieve a better, general-purpose sentence embedding model. |
| Outcome: | The proposed framework achieves state-of-the-art results on downstream transfer tasks and performs competitively on semantic textual similarity tasks, using only unsupervised data. |