A Survey of the Usages of Deep Learning in Natural Language Processing

TL;DR

The paper surveys how deep learning has transformed NLP, spanning architectures, core tasks, and practical applications. It traces the evolution from CNNs and RNNs to attention-based Transformers, highlighting pretraining and encoder–decoder frameworks as central. Key findings include that Transformer-based pre-trained models (e.g., BERT, GPT) achieve state-of-the-art across many tasks, but data requirements and language coverage remain important challenges. The work emphasizes evaluation, cross-lingual transfer, and the need for broader multilingual resources, while anticipating continued progress driven by scalable pretraining and multilingual benchmarks.

Abstract

Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This survey provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to a number of applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.

Figures (4)

• Figure 1: Encoder--Decoder Architectures. While there are multiple options of encoders and decoders available, RNN variants are a common choice for each, particularly the latter. Such a network is shown in (a). Attention mechanisms, such as that present in (b), allow the decoder to determine which portions of the encoding are most relevant at each output step.
• Figure 2: Transformer Model. (a) shows a transformer with four "encoders" followed by four "decoders", all following a "positional encoder". (b) shows the inner workings of each "encoder", which contains a self-attention layer followed by a feed forward layer. (c) shows the inner workings of each "decoder", which contains a self-attention layer followed by an attentional encoder-decoder layer and then a feed forward layer.
• Figure 3: Publication Volume for Core Areas of NLP. The number of publications, indexed by Google Scholar, relating to each topic over the last decade is shown. While all areas have experienced growth, language modeling has grown the most.
• Figure 4: Publication Volume for Applied Areas of NLP. All areas of applied natural language processing discussed have witnessed growth in recent years, with the largest growth occurring in the last two to three years.