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About this paper
Amirsina Torfi, Rouzbeh A. Shirvani, Yaser Keneshloo, Nader Tavaf, Edward A. Fox
A comprehensive survey systematically reviews the advancements in Natural Language Processing driven by deep learning, categorizing various applications and highlighting how neural architectures have enabled significant performance improvements and automated feature engineering across diverse NLP tasks.
Key insights
- Deep learning has instigated a paradigm shift in NLP, largely by automating the process of learning meaningful feature representations from raw text data.
- Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), Sequence-to-Sequence models, and Attention Mechanisms are identified as foundational architectures driving advancements across NLP tasks.
- While deep learning has led to significant performance gains, persistent challenges include data scarcity for specific tasks, limitations of current evaluation metrics, and complexities in applying reinforcement learning to NLP.
Summary by alphaXiv.
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