9 papers with code • 2 benchmarks • 1 datasets
They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts.
We improve the performance significantly by evolving the model from multiclass classification to semi-supervised multi-task learning by leveraging the negative cases, domain- and task-adaptively pretrained ALBERT on customer contact texts, and a number of un-curated data with no labels.
We propose a new general training technique for attention mechanisms based on virtual adversarial training (VAT).
We propose an effective consistency training framework that enforces a training model's predictions given original and perturbed inputs to be similar by adding a discrete noise that would incur the highest divergence between predictions.
To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC).