Natural Language Processing

The Most Innovative Things Happening With NLP

A short write-up with quick insights on the latest trends in NLP

There is no limit to what revolutionary changes Artificial Intelligence or AI can bring to the humankind be it speaking, listening, writing or understanding our native language.

Natural Language Processing or NLP is a subset of AI that bridges the conversational gap existing between humans and computer by combining AI with computational linguistics to help computers seamlessly understand and analyze user’s speech inputs.

NLP can be primarily segmented into three main processes, namely Natural Language Understanding, Natural Language Generation, and Natural Language Interaction (NLU, NLG, and NLI).

The NLU process removes ambiguity and tries is to understand the text, nature and structure of the user’s words. The NLG then generates text automatically from the structured data in a readable format through text and sentence planning and grammar realization. The NLI is an automated voice reply or a typed response as an interaction piece from the computer.

The process of NLP is ever-evolving, and here is a list of the most innovative things happening currently in this engrossing sphere.

1. Deep Learning

It is one of the avid Machine Learning techniques and with the recent innovations, Deep Learning has reached a point with its capability to even outperform its mighty creators, the Humans.

Deep learning’s recognition accuracy level is phenomenal which helps consumer electronics meet user expectations. Its other wide-based applications include automated driving, medical research, aerospace and defence, etc.

• Techniques like Recurrent Neural Networks or RNN can provide us with more accurate categorization for using the results of parsing and are therefore gaining popularity in certain text analytics and entity tagging platforms.
• A Transformer is the first transduction model solely relying on self-attention to compute representations of its inputs and outputs without using sequence-aligned RNNs. The term ‘Transduction’ simply means the conversion of input sequences into output sequences. This novel architecture in NLP aims to solemnly solve all sequence-to-sequence problems while easily handling long-range dependencies at the same time. Therefore, these transformers have proved to be better than any other traditional NLP models so far in terms of performance and speed.
• BERT (Bidirectional Encoder Representations from Transformers) is a new model proposed by the researchers at Google AI Language and has since become the buzz of the NLP community. Its key innovation lies in the application of bidirectional training of these transformer models to language modelling.
• Generative Pre-trained Transformer 3 (GPT-3) is the third-generation language prediction model in the GPT-n series which has a capacity of around 175 billion Machine Learning parameters. The quality of the text generated by GPT-3 is so superior that it becomes difficult to distinguish from that written by a human, pointing towards both benefits as well as other related risks.

2. Integrated Chatbot

Almost a quarter of businesses have already installed a virtual assistant or popularly termed as the Integrated Chatbots utilizing NLP to connect with their customers. They can transform any website into a responsive helpdesk with zero waiting time and by delivering personalized user experience. This facilitates seamless customer service and therefore clients are embracing these intelligent assistants.

Further studies show that the chatbot market is predicted to reach up to $9.4 billion by 2024. These NLP solutions with increasing demand in the text to speech innovations have revolutionized a majority of enterprises to derive business intelligence and helping them make enormous profits.

3. Semantic Search

This search seeks to understand natural language the way we, humans would and engages both NLP and NLU to understand the centralized ideas contained behind the texts given as inputs for searching over the Internet. In the process, the search engine attempts to provide the most accurate results in terms of searcher’s intent, query context and relationship between words being typed in the search bar.

Semantic search has now evolved immensely due to the rapid rise in voice search as people find it convenient and time-saving to search through their voice commands rather than typing through their mobile devices.

So, as a content writer or a marketer, we need to create content about our business that clearly answers the most common queries while browsing results at the top of the page before searching for more details.

4. Reinforcement Learning

This is a semi-supervised Machine Learning model and is a technique to guide an agent to make decisions sequentially and interact with an environment to maximize the total rewards. As every decision is inter-dependent like a game of chess, so we need to give labels to these sequences of dependent decisions.

Reinforcement Learning or RL enables the machine or software agent to learn its behaviour based on feedback from the environment. A number of Natural Language Generation tasks are being inspected by taking up RL and their applications include in robotics, web system configuration, resources management in computer clusters, traffic light control, etc.

It has gained immense popularity being the mainstream algorithm for solving different games and also sometimes to achieve super-human performance.

With NLP’s quantum lead in technological advancements, text analytics turns out to be the most far-flung use case in 2020. The extensive areas encouraged by Deep Learning and Machine Learning boost text analytics which involves the Chatbots, Semantic search and Speech-to-text transformation. Natural Language technologies are therefore bound to increase the efficiency of communication through cognitive computing.

There you have it, the most ground-breaking trends in Natural Language Processing we all need to watch out for. Thanks for reading!

You may connect with me on LinkedIn and also check out this video to get quick highlights on the latest NLP trends, link: https://www.linkedin.com/posts/techvariable_next-big-thing-in-nlp-activity-6694506754144780288-ahTA

Publication: TechVariable