[论文解读] Natural Language Processing Advancements By Deep Learning: A Survey

Amirsina Torfi, Rouzbeh A. Shirvani+3 more|arXiv (Cornell University)|Mar 2, 2020

Topic Modeling参考文献 222被引用 197

一句话总结

A survey of how deep learning has transformed NLP, covering core tasks, architectures, representations, seq2seq models, RL approaches, datasets, and benchmarks, with discussion of challenges and open problems.

ABSTRACT

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.

研究动机与目标

• Survey the role of deep learning in NLP and motivate why DL is effective for language tasks.
• Summarize essential DL architectures (MLP, CNN, RNN/LSTM, autoencoders, GANs) and their NLP applications.
• Explain core NLP concepts: feature representations, sequence-to-sequence models, and evaluation methods.
• Catalog benchmark NLP datasets and common evaluation metrics used in DL-NLP research.
• Discuss applications across NLP tasks and highlight challenges, opportunities, and open problems in the field.

提出的方法

• Review and categorize deep learning architectures used in NLP (MLP, CNN, RNN/LSTM, autoencoders, GANs).
• Describe seq2seq encoder–decoder frameworks and training with cross-entropy loss and teacher forcing.
• Discuss reinforcement learning approaches to NLP, including policy gradient, actor–critic, and exposure bias issues.
• Explain feature representations (one-hot, CBOW/word embeddings, character-level embeddings) and word/document representations (PV/Doc2Vec, skip-thought).
• Summarize benchmark datasets and evaluation metrics (ROUGE, BLEU, METEOR) used in NLP research.
• Present a survey of NLP tasks where DL has delivered significant gains and outline open problems.

实验结果

研究问题

• RQ1 How do deep learning architectures enhance core NLP tasks (e.g., tagging, NER, SRL, MT, summarization)?
• RQ2 What are the benefits and limitations of sequence-to-sequence models in NLP and how can training be improved?
• RQ3 What role do representations and embeddings play in DL-based NLP performance across tasks?
• RQ4 What are the prevalent benchmark datasets and evaluation metrics driving DL-NLP progress?
• RQ5 What open challenges remain in applying deep learning to NLP and where are future opportunities?

主要发现

• Deep learning enables data-driven representations that can outperform traditional hand-crafted features in many NLP tasks.
• Seq2seq models with encoder–decoder structures are central to MT, summarization, and other sequence tasks, with cross-entropy loss and beam search common during decoding.
• Character- and subword-level embeddings help address out-of-vocabulary issues and improve robustness in NLP applications.
• Reinforcement learning approaches (e.g., policy gradient, actor–critic) address exposure bias and objective-function misalignment but face training challenges due to large action spaces.
• A wide range of benchmark datasets and evaluation metrics (e.g., ROUGE, BLEU, METEOR) underpin DL-NLP research and benchmarking.
• The survey highlights ongoing challenges and promising directions in DL for NLP, including data representation, model architectures, and evaluation.