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Class-Aware Contrastive Optimization for Imbalanced Text Classification
Computation and Language · Submitted 29 Oct 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In this paper, we show that leveraging class-aware contrastive optimization combined with denoising autoencoders can successfully tackle imbalanced text classification tasks, achieving better performance than the current state-of-the-art.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
The unique characteristics of text data make classification tasks a complex problem. Advances in unsupervised and semi-supervised learning and autoencoder architectures addressed several challenges. However, they still struggle with imbalanced text classification tasks, a common scenario in real-world applications, demonstrating a tendency to produce embeddings with unfavorable properties, such as class overlap. In this paper, we show that leveraging class-aware contrastive optimization combined with denoising autoencoders can successfully tackle imbalanced text classification tasks, achieving better performance than the current state-of-the-art. Concretely, our proposal combines reconstruction loss with contrastive class separation in the embedding space, allowing a better balance between the truthfulness of the generated embeddings and the model's ability to separate different classes. Compared with an extensive set of traditional and state-of-the-art competing methods, our proposal demonstrates a notable increase in performance across a wide variety of text datasets.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The unique characteristics of text data make classification tasks a complex problem. | background 1.00 | 0.05 | 0.01 | asserted 1.00 |
| s1 | Advances in unsupervised and semi-supervised learning and autoencoder architectures addressed several challenges. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | However, they still struggle with imbalanced text classification tasks, a common scenario in real-world applications, demonstrating a tendency to produce embeddings with unfavorable properties, such as class overlap. | gap 0.78 | 0.08 | 0.03 | asserted 0.99 |
| s3 | In this paper, we show that leveraging class-aware contrastive optimization combined with denoising autoencoders can successfully tackle imbalanced text classification tasks, achieving better performance than the current state-of-the-art. | contribution 0.95 | 0.95 | 0.95 | asserted 1.00 |
| s4 | Concretely, our proposal combines reconstruction loss with contrastive class separation in the embedding space, allowing a better balance between the truthfulness of the generated embeddings and the model's ability to separate different classes. | method 0.99 | 0.88 | 0.09 | asserted 0.95 |
| s5 | Compared with an extensive set of traditional and state-of-the-art competing methods, our proposal demonstrates a notable increase in performance across a wide variety of text datasets. | result 1.00 | 0.95 | 0.20 | asserted 1.00 |
What was examined, and what was not
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The paper
Original abstract
The unique characteristics of text data make classification tasks a complex problem. Advances in unsupervised and semi-supervised learning and autoencoder architectures addressed several challenges. However, they still struggle with imbalanced text classification tasks, a common scenario in real-world applications, demonstrating a tendency to produce embeddings with unfavorable properties, such as class overlap. In this paper, we show that leveraging class-aware contrastive optimization combined with denoising autoencoders can successfully tackle imbalanced text classification tasks, achieving better performance than the current state-of-the-art. Concretely, our proposal combines reconstruction loss with contrastive class separation in the embedding space, allowing a better balance between the truthfulness of the generated embeddings and the model's ability to separate different classes. Compared with an extensive set of traditional and state-of-the-art competing methods, our proposal demonstrates a notable increase in performance across a wide variety of text datasets.
Human review
How the literature uses it
What this paper does with the arXiv papers it cites
What it cites · 16 sentences without a confident label
Uses the method or tool of 3 papers
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We define the reconstruction loss ($_Recon$) as a cross-entropy loss designed to measure how well the original sentence is reconstructed, leveraging previous work [ref] :
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning · Class-aware Contrastive Loss Computation · 0.90
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We have used the SentenceTransformers Python library [ref] and PyTorch to implement this loss function.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks · Baselines · 1.00
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We combined Doc2Vec [ref] embeddings, with an SVM-based classifier.
Distributed Representations of Sentences and Documents · Baselines · 0.99
Cites as support 2 papers
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Additionally, applying data augmentation techniques to imbalanced datasets may lead to model biases against minority classes [ref] and possibly to direct societal impact [ref] .
Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control · Introduction · 0.99
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Additionally, applying data augmentation techniques to imbalanced datasets may lead to model biases against minority classes [ref] and possibly to direct societal impact [ref] .
The Effects of Regularization and Data Augmentation are Class Dependent · Introduction · 0.99
Cites as background 12 papers
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Furthermore, a notion of supervised contrastive loss based on distances of samples from different classes was proposed for computer vision tasks [ref] .
Supervised Contrastive Learning · Related Work · 0.85
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Starting with SMOTE [ref] , these techniques have focused on synthesizing new data examples not previously seen in text data.
SMOTE: Synthetic Minority Over-sampling Technique · Related Work · 0.88
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Transformer-based model architectures have produced a new state-of-the-art (SOTA) performance on many natural language processing tasks such as machine translation [ref] , question answering [ref] , text generation [ref] , text-to-image translation [ref] , as well as a variety of text classification and information extraction tasks [ref] .
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks · Introduction · 0.98
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