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Found 8 papers, 2 papers with code
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.
A Simplified Fully Quantized Transformer for End-to-end Speech Recognition
While significant improvements have been made in recent years in terms of end-to-end automatic speech recognition (ASR) performance, such improvements were obtained through the use of very large neural networks, unfit for embedded use on edge devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition
Word-embeddings are vital components of Natural Language Processing (NLP) models and have been extensively explored.
TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks
Text generation is of particular interest in many NLP applications such as machine translation, language modeling, and text summarization.
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.
SALSA-TEXT : self attentive latent space based adversarial text generation
Inspired by the success of self attention mechanism and Transformer architecture in sequence transduction and image generation applications, we propose novel self attention-based architectures to improve the performance of adversarial latent code- based schemes in text generation.
