Unsupervised Sentence Representation Learning with Syntactically Aligned Negative Samples (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to sentence representation learning often encounter semantic inconsistencies and feature suppression. |
| Approach: | They propose a method for generating syntactically aligned negative (SAN) samples using a semantic importance-aware Masked Language Model (MLM) approach. |
| Outcome: | The proposed method produces negative samples with substantial textual overlap with the original sentences while conveying different meanings. |
Similar Papers
Debiased Contrastive Learning of Unsupervised Sentence Representations (2022.acl-long)
Copied to clipboard
| Challenge: | Recent studies have shown that contrastive learning improves pre-trained language models to derive high-quality sentence representations. |
| Approach: | They propose a framework to punish false negatives and generate noise-based negatives to guarantee the uniformity of the representation space. |
| Outcome: | The proposed framework improves pre-trained language models while pushing apart irrelevant negatives to guarantee the uniformity of the representation space. |
Clustering-Aware Negative Sampling for Unsupervised Sentence Representation (2023.findings-acl)
Copied to clipboard
| Challenge: | Using clustering-aware learning, in-batch negatives are often ignored in sentence representation learning. |
| Approach: | They propose a method that integrates cluster information into contrastive learning for unsupervised sentence representation learning. |
| Outcome: | The proposed method compares favorably with baselines on semantic textual similarity tasks. |
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding (2022.findings-acl)
Copied to clipboard
| Challenge: | Unsupervised contrastive learning is emerging as a powerful technique for extracting knowledge from unlabeled data. |
| Approach: | They propose a momentum contrastive learning model with negative sample queue for sentence embedding with a simulated model with EMA update mechanism. |
| Outcome: | The proposed model achieves a Spearman’s correlation of 77.27% on the semantic text similarity task and a maximum traceable distance metric. |
Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings (2025.acl-long)
Copied to clipboard
| Challenge: | Text embedding models are used for various natural language processing tasks such as sentiment analysis, text clustering, and content-based information retrieval. |
| Approach: | They propose a synthesis framework that leverages large language models to generate diverse negative samples with varying levels of similarity with the query. |
| Outcome: | The proposed framework achieves state-of-the-art performance surpassing existing synthesis strategies with synthetic data and when combined with public retrieval datasets. |
Trainable Hard Negative Examples in Contrastive Learning for Unsupervised Abstractive Summarization (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for contrastive learning rely on manual negative examples and are poor in quality and adaptability during training. |
| Approach: | They propose a framework that learns trainable negative examples for contrastive learning in unsupervised abstractive summarization by combining a negative example network and a representation network. |
| Outcome: | The proposed approach eliminates the need for manual negative example design and improves on two benchmark datasets. |
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to retrieve hard negative sentences are limited in the scale of the dataset thus fail to identify negative samples of high difficulty for every image. |
| Approach: | They propose to use a model to generate synthetic negative sentences with higher difficulty by masking and refilling the images and performing word discrimination and word correction tasks to improve retrieval and generation. |
| Outcome: | The proposed model generates synthetic negative sentences with higher difficulty on MS-COCO and Flickr30K and is robust and faithful to state-of-the-art training. |
SALAD: Improving Robustness and Generalization through Contrastive Learning with Structure-Aware and LLM-Driven Augmented Data (2025.naacl-long)
Copied to clipboard
| Challenge: | In many natural language processing tasks, model training often leads to spurious correlations . shortcuts allow models to rely on irrelevant patterns in the data, leading to biased predictions. |
| Approach: | They propose a method to generate structure-aware positive and negative sentences using tagging. |
| Outcome: | The proposed method improves model robustness and generalization across different environments while minimizing spurious correlations. |
Capturing the Relationship Between Sentence Triplets for LLM and Human-Generated Texts to Enhance Sentence Embeddings (2024.findings-eacl)
Copied to clipboard
| Challenge: | Recent advances in building sentence embedding models have centered on replacing traditional human-generated text datasets with those generated by LLMs. |
| Approach: | They propose a loss function that incorporates Positive-Negative sample Augmentation within the contrastive learning objective to enhance sentence embeddings using both human and LLM-generated datasets. |
| Outcome: | The proposed model mitigates the sentence anisotropy problem in Wikipedia corpus and improves Spearman’s correlation in standard Semantic Textual Similarity (STS) tasks (+1.47% compared to CLHAIF). |
Alleviating Over-smoothing for Unsupervised Sentence Representation (2023.acl-long)
Copied to clipboard
| 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. |
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. |