Semi-Supervised Text Classification
22 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Text Classification
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AG News
HeriGraph
Most implemented papers
Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses
GT-SALT/AAAI_CLF
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We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses.
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
DevSinghSachan/ssl_text_classification
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AAAI 2019 2019
In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches.
Consistency Training with Virtual Adversarial Discrete Perturbation
Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar.
FLiText: A Faster and Lighter Semi-Supervised Text Classification with Convolution Networks
valuesimplex/flitext
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EMNLP 2021
In natural language processing (NLP), state-of-the-art (SOTA) semi-supervised learning (SSL) frameworks have shown great performance on deep pre-trained language models such as BERT, and are expected to significantly reduce the demand for manual labeling.
Semi-Supervised Text Classification via Self-Pretraining
p-karisani/self_pretraining
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This set is used to initialize the second classifier, to be further trained by the set of labeled documents.
ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification
Our ME-GCN can integrate a rich source of graph edge information of the entire text corpus.
Progressive Class Semantic Matching for Semi-supervised Text Classification
Semi-supervised learning is a promising way to reduce the annotation cost for text-classification.
SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training
declare-lab/sat
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Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning.
Rank-Aware Negative Training for Semi-Supervised Text Classification
amurtadha/rnt
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To alleviate the noisy information, we adapt a reasoning with uncertainty-based approach to rank the unlabeled texts based on the evidential support received from the labeled texts.
SemiReward: A General Reward Model for Semi-supervised Learning
The main challenge is how to distinguish high-quality pseudo labels against the confirmation bias.
