Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework (2022.emnlp-main)
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| Challenge: | Existing supervised sentence embedding techniques rely on expensive human-annotated sentence pairs as the supervised signals. |
| Approach: | They propose a semi-supervised sentence embedding framework that leverages large-scale unlabeled data. |
| Outcome: | The proposed framework surpasses state-of-the-art methods on four domain adaptation tasks. |
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Differentiable Data Augmentation for Contrastive Sentence Representation Learning (2022.emnlp-main)
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| Challenge: | a contrastive learning framework is used to fine-tune pre-trained language models with unlabeled sentences or labeled sentences. |
| Approach: | They propose a method that makes hard positives from unlabeled sentences . they use a prefix attached to a model to allow for differentiable data augmentation . |
| Outcome: | The proposed method yields significant improvements over existing methods under semi-supervised and supervised settings. |
Contrastive Learning of Sentence Embeddings from Scratch (2023.emnlp-main)
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| Challenge: | Existing approaches to learn sentence embeddings with unlabeled data are limited due to copyright restrictions, data distribution issues, and messy formats. |
| Approach: | They propose a contrastive learning framework that trains sentence embeddings with synthetic data. |
| Outcome: | The proposed framework produces positive and negative annotations given unlabeled sentences and generates sentences along with their corresponding annotations from scratch. |
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
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| 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. |
Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning (2024.naacl-long)
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| Challenge: | Existing methods for training contrastive learning based sentence embedding models are largely influenced by the quality of sentence pairs. |
| Approach: | They propose a framework that decomposes LLMs into three stages for training . they propose to refine the generated content at these stages to ensure only high-quality sentence pairs are utilized to train a base contrastive learning model. |
| Outcome: | The proposed framework surpasses ChatGPT and ChatGPP in terms of performance. |
Virtual Augmentation Supported Contrastive Learning of Sentence Representations (2022.findings-acl)
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| Challenge: | Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain-specific knowledge. |
| Approach: | They propose a virtual augmentation supported Contrastive Learning of sentence representations . they approximate the neighborhood of an instance via its K-nearest in-batch neighbors . |
| Outcome: | The proposed model outperforms existing methods on a wide range of downstream tasks. |
Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning (2022.findings-emnlp)
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| 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. |
Hyper-CL: Conditioning Sentence Representations with Hypernetworks (2024.acl-long)
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| Challenge: | Existing approaches to sentence embeddings do not capture fine-grained semantics of sentences. |
| Approach: | They propose a method that integrates hypernetworks with contrastive learning to generate conditioned sentence representations. |
| Outcome: | The proposed method narrows the performance gap with the bi-encoder architecture while maintaining the time efficiency characteristic of the tri-encoding approach. |
GASE: Generatively Augmented Sentence Encoding (2025.findings-emnlp)
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| Challenge: | Generatively Augmented Sentence Encoding variates the input text by paraphrasing, summarizing, or extracting keywords, followed by pooling the original and synthetic embeddings. |
| Approach: | They propose a training-free approach to improve sentence embeddings by applying generative text models for data augmentation at inference time. |
| Outcome: | The proposed approach does not require access to model parameters or computational resources typically required for fine-tuning state-of-the-art models. |
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer (2021.acl-long)
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| Challenge: | Existing BERT-based pre-trained language models achieve high performance on many downstream tasks, but native derived sentence representations are collapsed and thus poor performance on semantic textual similarity (STS) tasks. |
| Approach: | They propose a framework for self-supervised Sentence Representation Transfer that adopts contrastive learning to fine-tune BERT in an unsupervised way. |
| Outcome: | The proposed framework improves on the BERT-derived representations by 8% on STS datasets and shows robustness in data scarcity scenarios. |
SumCSE: Summary as a transformation for Contrastive Learning (2024.findings-naacl)
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| Challenge: | Sentence embedding models are typically trained using contrastive learning (CL) using human annotations directly or by repurposing other annotated datasets. |
| Approach: | They propose to use generative language models to generate CL data using annotated data. |
| Outcome: | The proposed method outperforms the previous best unsupervised method by 1.8 points and SimCSE, a strong supervised baseline by 0.3 points on the semantic text similarity (STS) benchmark. |