The Role of NLP in Global Communications

welocalize March 10, 2022

Imagine this: You’re about to go to bed, and you launch Siri. You say, “Set alarm for 7 a.m.,” and Siri says, “Your alarm is set for 7 a.m. tomorrow.”

That exchange with your voice assistant is possible primarily because of natural language processing (NLP).

NLP plays a crucial role in a program or machine’s ability to process human speech. It’s the technology behind many of the current global communications systems and processes.

Learn more about the importance of NLP and its use in multilingual communications and content in this blog post.

What Is NLP and Why Its Importance Is Growing

NLP is a branch of computer science, specifically artificial intelligence (AI). It helps computers analyze the way humans speak and write. NLP is a challenging task since natural language involves tons of unstructured data. Plus, the style in which people write and talk is often unique and constantly changing to reflect common usage.

Understanding context is also challenging since processing natural language requires semantic analysis for machine learning (ML) to understand the content.

This is where natural language understanding (NLU) comes in. NLU is a sub-branch of NLP that handles human speech and written text nuances. This field tries to develop reading comprehension for machines instead of simply processing text independently from the meaning.

Simply put, NLP can process text from structure, grammar, point of view, and typo, but NLU helps the machine infer the intent and semantic information behind language text.

The goal of NLP and NLU is to allow computers to understand the human language well enough to converse naturally.

NLP and NLU are critical because of their application in modern and constantly evolving technologies across industries and processes. This is true from business and health to global communications.

Use of NLP in Multiple Languages for Multilingual Communications

As businesses and other entities continue to expand across borders, the use of NLP has become indispensable for technologies used in multilingual communications and processes.

One of the crucial applications of NLP is in language translation. NLP is behind computer programs that translate text from one language to another (or multiple languages).

For instance, machine translation (MT) is an NLP task where a model learns how to transform written content from a source language into a target language. Currently, this task is achieved by neural models trained on massive amounts of parallel data (millions of translated sentences). Internally, this type of model breaks up source sentences into units of language (words or subwords). These units (called tokens) are then converted into vectors of numbers passed into the neural network which finally produces the translation. Virtual assistants, such as Amazon’s Alexa and Apple’s Siri, use automatic speech recognition models to transform voice commands into text so that it can be processed to understand the intent from the user (in voice commands), provide appropriate responses through natural language generation and carry out expected actions from the userby selecting the most probable words in the target language. 

Watch You & AI LinkedIn Live interview on Voice AI, featuring Tiarne Hawkins, Welocalize AI Director, and Adam Cheyer, Co-Founder of Siri.

Other Use Cases of NLP

Besides machine translation, NLP powers many AI-based technologies across modern, real-world applications, including the following:

Chatbots and Virtual Agents

The apps can learn to recognize contextual clues about human requests to give better options and responses over time.

Text Summarization

Text summarization uses NLP techniques to digest massive data volumes of digital text. It then creates synopses and summaries for research databases, indexes, and readers who don’t want to read the full text.

Dedicated text summarization apps use natural language generation (NLG) to add relevant conclusions and context to summaries.

Sentiment Analysis on Social Media

Sentiment analysis is an NLP technique that analyzes language used in social media responses, posts, and reviews.

The goal of sentiment analysis is to determine the emotions and attitudes of social media users in response to promotions, events, and products. Companies use the information to develop and improve ad campaigns, product designs, content, and more.

The Future of NLP

NLP will only play a bigger and more crucial role as more companies and entities aim to make sense of volumes of data for multilingual communications and other purposes.

Businesses can leverage NLP-driven technology to analyze large-scale data to determine customer pain points and deliver relevant multilingual content to gain more global consumers.

NLP-based technologies as a critical aspect of AI will continue to be the driving force that fuels data and intelligence-driven endeavors. This will shape and improve multilingual communication technologies in the future.

Take the Next Step to Effective Multilingual Communications

Partnering with experts is an excellent way to ensure your company achieves an effective and efficient global communications process and system.

Work with language service providers with reliable NLP and AI techniques and technologies to get multilingual communications and content that drive profit and business growth.

Contact us today, and find out how Welocalize can help you take your AI and content global.

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