Recent Trends In Deep Learning Based Natural Language Processing

The advancement of digital technologies has enabled machines to mimic human intelligence in terms of learning, reasoning and problem solving. This is known as artificial intelligence or AI and there are several subsets of the technology. Deep learning and natural language processing (NLP) are two of them.

NLP gives machines the ability to understand text and spoken words in a similar way to humans and combines computational linguistics with statistical machine learning and deep learning models. The combination of these technologies give machines the ability to understand words and the intent and sentiment behind them.

The use of NLP can be seen in GPS systems, digital assistants, and chatbots. While the technology is still in the process of evolving and improving, these are recent deep learning based NLP trends any machine learning consultancy should look out for.

• Relevancy Of Embedding Approaches

The digital technology industry is never stagnant. There are constant changes and improvements being made to technologies like deep learning and NLP. These developments will give way to new approaches and models, which can carry out functions efficiently and quickly. The simplifying of processes is always welcome, which was seen with the transformer model introduced in 2017.

However, this does not mean that older approaches and practices will stop being relevant. In terms of embedding approaches, word embedding approaches like GloVe are expected to continue to play a key role in deep learning based natural language processing.

This is not to say that all approaches will remain in use. Some approaches may have certain limitations and disadvantages but a trend that can be seen in the industry is the use of a combination of the old and new with regard to embedding and other approaches and this is something AI service providers should make note of this year.

• Recurrent Neutral Networks

Recurrent Neutral Networks or RNNs are described as a class of artificial neutral networks where connections between nodes form a directed graph along a temporal sequence. They have acted as the standard architecture for NLP-based neutral networks but recent trends show a shift from this.

While RNNs have been a key technology in deep learning, Embedding from Language Models or ELMo is an approach that is being used by any AI app development company that wants to provide clients with high quality AI services.

ELMo is described as a deep contextualised word representation that models complex characteristics of word use and the way in which these uses vary across linguistic contexts. The key features of ELMo include representations that are contextual, deep, and character based.

Like many other relatively older approaches, RNNs will remain relevant and will be in use this year but may not hold as much prominence in deep learning based natural language processing.

• Transformer Model

Introduced in 2017, the transformer model is based on encoder-decoder architecture but has been simplified in comparison to other models.

While the transformer model has been in use for the past few years, recent trends in deep learning based natural language processing suggests that the transformer model will play a more dominant role in the industry. This is backed by the introduction of Transformer-XL in 2019.

The report titled “Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context” states, “Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modelling.” To address these limitations, the authors proposed Transformer-XL, which enables learning dependency beyond a fixed length without disrupting temporal coherence.

• Pre-trained Models

Recent trends also show that pre-trained models may develop more general linguistic skills and two key factors play a key role in this regard. The first factor is how the transformer model and other new architectures will make it easier to train models on datasets which may have once been considered too large and computationally expensive to learn from.

The other factor to consider is the launch of models like ELMo, TensorFlow Hub, and Universal Sentence Encoder (USE). With more improvements made to these concepts, it is expected that this trend too, will play a prominent role this year.