The Future of Natural Language Processing: Trends and Predictions
01/03/2023 | 2 Min Read
Natural language processing (NLP) is an artificial intelligence subfield that deals with the interaction between computers and human languages.
It involves techniques for analyzing, generating, and understanding human language to enable communication between humans and machines.
In the recent past, there has been tremendous progress in NLP. That’s made it become an integral part of many applications. Language translation, voice assistants, and chatbots have benefited from that progress.
In this article, we will explore the current state of NLP and its future trends and predictions. We will also discuss the challenges and opportunities in this rapidly developing field.
What Is the Current State of NLP?
NLP has come a long way since its early days. In the past, NLP systems were rule-based and required much manual effort to create the rules.
With the advancement of machine learning, more automation in NLP has made it even more accurate.
One breakthrough in NLP was the development of word embeddings. Word embeddings are dense vectors representing words in a continuous vector space that capture the semantic relationships between phrases.
Word embeddings have improved the performance of many NLP tasks, such as language translation, modeling, and text classification.
Another significant advancement in NLP was deep learning. Deep learning has led to the development of powerful neural network models that can learn from large amounts of data.
These models, such as long short-term memory (LSTM) and transformer, have achieved state-of-the-art results on many NLP tasks. That’s made them the backbone of many modern NLP systems.
One thing is clear: the future of natural language processing holds a lot of promise and probably some surprises too.
The only real question is, what will we use this technology for in the future? Well, look above at our seven predictions, and take a guess.
7 Trends and Predictions In NLP
1. Increased Use of Pre-Trained Models
One of the significant trends in NLP is the use of pre-trained models. These models have received training on large amounts of data, making them easy to fine-tune for specific tasks.
Pre-trained models have several advantages:
- They need less training time
- Offer better performance and
- Can handle a wide range of tasks.
A significant example is Google’s BERT (Bidirectional Encoder Representations from Transformers). Bert is a pre-trained model with state-of-the-art results on many NLP tasks, such as language understanding, translation, and question-answering.
The NLP community has adopted it in droves, making it the go-to model for many tasks.
2. Continued Improvement in Languagem Translation
Language translation is a crucial application of NLP, and there has been significant progress in this area in recent years. Machine translation systems have become much more accurate and can handle many languages.
One of the significant trends in language translation is using neural machine translation (NMT). This machine translation uses neural networks to learn the relationship between the source and target languages. NMT has significantly improved translation quality and has become the dominant machine translation approach.
3. Greater Adoption of Voice Assistants
Voice assistants, such as Amazon Alexa and Google Assistant, have become increasingly popular in recent years. Experts expect this trend to continue.
Forecasts say there will be more voice assistants than the human population by 2024( over 8.4 billion). These assistants use NLP to understand and respond to voice commands and queries.
Voice assistants have the potential to revolutionize the way we interact with computers and devices. They offer a more natural and convenient way to communicate with machines, and they will probably become an integral part of many applications in the future.
4. More Intelligent Chatbots
Source: Getty Images via Canva
Chatbots are computer programs that use NLP to communicate with humans in natural language. Unless you’ve just landed on planet earth, you know the impact chatbots have on customer service. Chatbots have changed customer service, sales, and other areas to automate customer interactions.
In the past, businesses would converse with their clients via phone calls or other less conventional means. Chatbots have revolutionized business communication even though they are only in their infancy. Indeed marketing platform in a study found that 80% of customers have had a positive experience with chatbots.
More chatbots will depend on NLP to function. Therefore, the development of NLP technology will also change how companies use their chatbots.
More intelligent chatbots could become the thing in the future.
5. More Companies Will Invest in NLP Technology
It’s difficult not to see this happening. Many companies have already jumped on the bandwagon that’s artificial intelligence technology. However, for NLP, the use hasn’t been as widespread.
However, that could change with Market Research predicting the NLP industry could be worth as much as $50 billion in 2027. Industry giants Google and Facebook will invest more in the technology to see further advancements.
Advancements in machine learning and AI technologies will catalyze this growth.
6. The Rise of Better Transformer Models
Google’s BERT has been around for some time, but more companies are investing in better transformer models. Open AI’s GPT-3 recent development, ChatGPT could blow many transformer models out of the water.
GPT-3 can easily converse with humans, translate, extract, and perform more NLP functions.
Probably the most famous example of GPT -3’s ability is ChatGPT. ChatGPT is a chatbot that has taken the world by storm since its launch just a few months back.
The chatbot uses reinforced and supervised learning to answer questions and converse with humans. It’s still in the testing phase and thus free for use by anyone, but it still has everyone talking.
See more about ChatGPT here
What is ChatGPT? Chat GPT Explained (with AI chatbot examples)
7. Further Development of Multilingual NLP
There has been a growing focus on developing NLP models that can handle multiple languages, either through the use of multilingual pre-trained models or through the use of transfer learning techniques
This could become a significant development in NLP for the next few years because of its importance to many real-world applications. Most social media platforms and search engines involve text data from a diverse range of languages and language varieties.
Transfer learning techniques and the use of pre-trained models will spearhead this development. The two are already in use, but they can only get better from here.
8. Google, Facebook, and Dialogue Models
Where technology is involved, you won’t miss Google and Facebook. Indeed, the two tech giants are investing millions of dollars in research on improved dialogue models that could be the future of NLP.
Google has developed LAMDA, a conversational AI system that exploits the narrowness of chatbots. The demonstration is capable of deeper conversations and could be the disruption future customer support and help desk experts want.
Source: Getty Images
Natural Language Processing Has a Bright Future
Although NLP has grown in leaps and bounds since its inception, there’s room for further growth. Continuous investment into NLP research will lead to more advancements in the field.
It won’t be surprising to see the use of NLP taken up by many companies as they attempt to offer customers the best service.
Are you looking to jump onto the NLP bandwagon? We help train NLP applications that could have far-reaching impacts on organizational operations.
Reach out to our expert, and let’s talk about how we can help with your NLP model.