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
Latest papers
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.
Consistency Training with Virtual Adversarial Discrete Perturbation
Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar.
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.
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix.
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.
Semi-Supervised Learning with Normalizing Flows
izmailovpavel/flowgmm
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ICML 2020
Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood.
Variational Pretraining for Semi-supervised Text Classification
We accompany this paper with code to pretrain and use VAMPIRE embeddings in downstream tasks.
Adversarial Dropout for Recurrent Neural Networks
sungraepark/adversarial_dropout_text_classification
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Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs).
Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings
We propose a novel and simple method for semi-supervised text classification.
Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
The first, critical, task for these applications is classifying whether a personal health event was mentioned, which we call the (PHM) problem.
