A Survey of Deep Learning Techniques for Neural Machine Translation
arXiv:2002.07526
Abstract
In recent years, natural language processing (NLP) has got great development with deep learning techniques. In the sub-field of machine translation, a new approach named Neural Machine Translation (NMT) has emerged and got massive attention from both academia and industry. However, with a significant number of researches proposed in the past several years, there is little work in investigating the development process of this new technology trend. This literature survey traces back the origin and principal development timeline of NMT, investigates the important branches, categorizes different research orientations, and discusses some future research trends in this field.
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- A Survey of Knowledge-Enhanced Text Generation
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- Communication-Efficient Distributed Deep Learning: A Comprehensive Survey
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- FarsTail: A Persian Natural Language Inference Dataset
- Hindi to English: Transformer-Based Neural Machine Translation
- Literature review on vulnerability detection using NLP technology
- Comparative Analysis of Agent-Oriented Task Assignment and Path Planning Algorithms Applied to Drone Swarms
- Deep Learning with Kernel Flow Regularization for Time Series Forecasting
- PSG: Prompt-based Sequence Generation for Acronym Extraction
- Revisiting Robust Neural Machine Translation: A Transformer Case Study
- Zero-Shot Controlled Generation with Encoder-Decoder Transformers