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Do we need Label Regularization to Fine-tune Pre-trained Language Models?
Knowledge Distillation (KD) is a prominent neural model compression technique that heavily relies on teacher network predictions to guide the training of a student model.
From Unsupervised Machine Translation To Adversarial Text Generation
B-GAN is able to generate a distributed latent space representation which can be paired with an attention based decoder to generate fluent sentences.
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation
This soft representation will be used in GAN discrimination to synthesize similar soft-texts.
Bilingual-GAN: A Step Towards Parallel Text Generation
Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively.
A Self-Training Method for Semi-Supervised GANs
In this work, we combine these two ideas and make GANs self-trainable for semi-supervised learning tasks by exploiting their infinite data generation potential.
