Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

By Tom Young, Devamanyu Hazarika, Soujanya Poria, Erik Cambria. 2018

Subfields: Large Language Models, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Large Language Models

1. Dongmei Zhang

In Natural Language Processing

1. Shi Han
2. Lun Du
3. Sneha Chaudhari
4. Dongmei Zhang
5. Yuxuan Hu
6. Varun Mithal
7. Ensheng Shi
8. Rohan Ramanath

Citations in the corpus

• CoCoGUM: Contextual Code Summarization with Multi-Relational GNN on UMLs (2020, background): “Recurrent Neural Networks (RNNs) [23], Long Short-term Memory Networks (LSTMs) [24], Gated Recurrent Units (GRUs) [6], Transformers [25] or other deep neural networks [26], which are capable of modeling sequential data, are are widely used for the encoder and the decoder in such a design.”
• Machine learning and deep learning (2021, background): “Example applications are sentiment analysis or machine-based translation and summarization of documents (Young et al. 2018).”
• An Attentive Survey of Attention Models (2019, background): “First, these models are now the state-of-the-art [Young et al., 2018] for multiple tasks such as Machine Translation, Question Answering, Sentiment Analysis, Part-of-Speech tagging, Constituency Parsing and Dialogue Systems.”
• Ultra-Low Precision 4-bit Training of Deep Neural Networks (2020, background): “Over the past decade, Deep Neural Networks (DNNs) have surpassed traditional Machine Learning techniques on a wide spectrum of application domains including speech [1–3], computer vision [4–10], and language [11, 12].”