An In-depth Walkthrough on Evolution of Neural Machine Translation
arXiv:2004.04902
Abstract
Neural Machine Translation (NMT) methodologies have burgeoned from using simple feed-forward architectures to the state of the art; viz. BERT model. The use cases of NMT models have been broadened from just language translations to conversational agents (chatbots), abstractive text summarization, image captioning, etc. which have proved to be a gem in their respective applications. This paper aims to study the major trends in Neural Machine Translation, the state of the art models in the domain and a high level comparison between them.
10 pages, 10 figures
References in corpus (7)
- Sequence to Sequence Learning with Neural Networks
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- On the difficulty of training Recurrent Neural Networks
- Convolutional Sequence to Sequence Learning
- Self-Attention Generative Adversarial Networks
- A Structured Self-attentive Sentence Embedding
- A Deep Reinforced Model for Abstractive Summarization