Clustering-Aware Negative Sampling for Unsupervised Sentence Representation

• URL: http://arxiv.org/abs/2305.09892v1
• Date: Wed, 17 May 2023 02:06:47 GMT
• Title: Clustering-Aware Negative Sampling for Unsupervised Sentence Representation
• Authors: Jinghao Deng and Fanqi Wan and Tao Yang and Xiaojun Quan and Rui Wang
• Abstract summary: ClusterNS is a novel method that incorporates cluster information into contrastive learning for unsupervised sentence representation learning. We apply a modified K-means clustering algorithm to supply hard negatives and recognize in-batch false negatives during training.
• Score: 24.15096466098421
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrastive learning has been widely studied in sentence representation learning. However, earlier works mainly focus on the construction of positive examples, while in-batch samples are often simply treated as negative examples. This approach overlooks the importance of selecting appropriate negative examples, potentially leading to a scarcity of hard negatives and the inclusion of false negatives. To address these issues, we propose ClusterNS (Clustering-aware Negative Sampling), a novel method that incorporates cluster information into contrastive learning for unsupervised sentence representation learning. We apply a modified K-means clustering algorithm to supply hard negatives and recognize in-batch false negatives during training, aiming to solve the two issues in one unified framework. Experiments on semantic textual similarity (STS) tasks demonstrate that our proposed ClusterNS compares favorably with baselines in unsupervised sentence representation learning. Our code has been made publicly available.

Related papers

• Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives [17.90820242798732]
  Unsupervised contrastive learning methods still lag far behind the supervised counterparts. We propose switch-case augmentation to flip the case of the first letter of randomly selected words in a sentence. For negative samples, we sample hard negatives from the whole dataset based on a pre-trained language model.
  arXiv  Detail & Related papers  (2022-06-06T09:46:12Z)
• Debiased Contrastive Learning of Unsupervised Sentence Representations [88.58117410398759]
  Contrastive learning is effective in improving pre-trained language models (PLM) to derive high-quality sentence representations. Previous works mostly adopt in-batch negatives or sample from training data at random. We present a new framework textbfDCLR to alleviate the influence of these improper negatives.
  arXiv  Detail & Related papers  (2022-05-02T05:07:43Z)
• Investigating the Role of Negatives in Contrastive Representation Learning [59.30700308648194]
  Noise contrastive learning is a popular technique for unsupervised representation learning. We focus on disambiguating the role of one of these parameters: the number of negative examples. We find that the results broadly agree with our theory, while our vision experiments are murkier with performance sometimes even being insensitive to the number of negatives.
  arXiv  Detail & Related papers  (2021-06-18T06:44:16Z)
• Incremental False Negative Detection for Contrastive Learning [95.68120675114878]
  We introduce a novel incremental false negative detection for self-supervised contrastive learning. During contrastive learning, we discuss two strategies to explicitly remove the detected false negatives. Our proposed method outperforms other self-supervised contrastive learning frameworks on multiple benchmarks within a limited compute.
  arXiv  Detail & Related papers  (2021-06-07T15:29:14Z)
• Solving Inefficiency of Self-supervised Representation Learning [87.30876679780532]
  Existing contrastive learning methods suffer from very low learning efficiency. Under-clustering and over-clustering problems are major obstacles to learning efficiency. We propose a novel self-supervised learning framework using a median triplet loss.
  arXiv  Detail & Related papers  (2021-04-18T07:47:10Z)
• Doubly Contrastive Deep Clustering [135.7001508427597]
  We present a novel Doubly Contrastive Deep Clustering (DCDC) framework, which constructs contrastive loss over both sample and class views. Specifically, for the sample view, we set the class distribution of the original sample and its augmented version as positive sample pairs. For the class view, we build the positive and negative pairs from the sample distribution of the class. In this way, two contrastive losses successfully constrain the clustering results of mini-batch samples in both sample and class level.
  arXiv  Detail & Related papers  (2021-03-09T15:15:32Z)
• Contrastive Learning with Hard Negative Samples [80.12117639845678]
  We develop a new family of unsupervised sampling methods for selecting hard negative samples. A limiting case of this sampling results in a representation that tightly clusters each class, and pushes different classes as far apart as possible. The proposed method improves downstream performance across multiple modalities, requires only few additional lines of code to implement, and introduces no computational overhead.
  arXiv  Detail & Related papers  (2020-10-09T14:18:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.