Natural Language Generation

1. What is Natural Language Generation?

NLG, an artificial intelligence (AI) subfield, is a software process that converts data into simple English content automatically. By writing the sentences and paragraphs for you, the technology would tell a story, just like that of a human analyst. NLG is one of the company's fastest-growing innovations being embraced. There are several use-cases for NLG, but when deployed to automate time-intensive data analysis and reporting activities, it is seen to be the most effective.

2. What’s the goal of Natural Language Generation?

People have always expressed ideas from data. However, the company must find creative ways to keep up with the flood of data that needs to be processed and interpreted, combined with growing pressures to minimize costs and satisfy consumer demands.

As it turns out, a computer can communicate ideas from data at an incredible scale and precision and can execute it in a particularly articulate way. If the more repetitive research and communication functions are automated by a computer, productivity improves and workers may concentrate on more high-value activities.

Although, NLG's bigger picture, if you may, is not about language but about managing the increasing number of insights provided by big data through automated analysis.

3. Evolution of NLG Design and Architecture

NLG systems used various methods and tricks in their attempts to imitate human speech and adapt to their writing style, sound, and structure according to the audience, meaning, and intent of the story. Reiter and Dale pipelined the NLG architecture in 2000, differentiating three phases in the NLG process:

A. Document planning:

Determining what to say and producing an abstract document detailing the knowledge framework to be submitted.

B. Micro planning:

Generation of referring terms, choice of words, and aggregation to fill out the requirements of the text.

C. Realization:

Translating the requirements of the abstract document to an actual text, using domain awareness of syntax, morphology, etc.

4. What are the Applications of Natural Language Generation?

Following are a few areas where natural language generation can be of great utility:  

A. Analytics Dashboards

Analytics dashboards are among the earliest and most visible application areas for the development of natural language. Business leaders also need data in an easily understandable format so that they can make decisions quickly and efficiently. They sometimes lack the time to pore through pages of analytical papers or look at a pile of charts and graphs in order to extract actionable insights. NLG-powered tools can also be used in succinct and detailed reports generated using the information collected to analyze the data generated from analytics. Leaders will always have access to the most up-to-date information for making critical decisions using such NLG-powered analytics dashboards.

B. Chatbots

The efficacy of a chatbot is primarily dependent on its ability to connect or communicate with individuals in a way that a person would. The best chatbots are the ones that give users the feeling that a real person is talking. These chatbots not only need specialized skills for the processing of natural language, but also the ability to generate effective natural language. Such chatbots can be highly context-sensitive and adept at personalizing user interactions, helping companies simplify verticals of their customer service. These chatbots can be utilized for various purposes, such as complaint and question settlement and virtual assistance for online processes (e.g., form filling).    

C. Content Creation

When machines are built that are capable of producing content with the naturalness and quirkiness of human authors, only then can AI research truly reach new heights. Although this may be too much to expect from AI as of now, these systems may at least help organizations produce technical, non-creative content, such as explanations of components and products, internal communication, contracts, and other similar forms of textual communication.

Despite NLG-equipped AI tools already being used in some of these areas, the degree of competence and intelligence that would make them truly autonomous is yet to be achieved. The growth and advancement of the generation of natural language would lead to multiple novel applications in various industries and spheres, but the greatest effect on the field of artificial intelligence itself will be not only in Natual Language Generation (NLG) but Natural Language Processing (NLP) and Natural Language Understanding (NLU) as well. That's because giving them the ability to interpret, understand, and communicate ideas in the human language would be an important step in the search to create machines that are human in every way, taking us closer than ever to creating true artificial intelligence.

5. How is NLG different from NLP?

The latest Hype Cycle for BI and Analytics from Gartner sums up the distinction between NLG and NLP well:

NLP focuses on deriving analytic insights from textual data, by integrating analytical production with contextualized narratives, whereas NLG is used to synthesize textual content.

In other words, when NLG writes, NLP reads. NLP mechanisms look at language to find out what meanings are being shared. NLG systems begin with a collection of information-locked ideas and in turn, convert them into a language that communicates with them.

6. What are the different variations of Natural Language Generation?

For most NLG platforms, the problem is that they hard-code intelligence into a template. This makes for systems that without new coding are brittle, difficult to modify, and are unable to accept new data.

For applications that involve the straightforward translation of data into text, templated NLG systems work well. However, for those who want to scalably communicate data-driven information, intelligent NLG systems need to be used that perform more than rules-based functions aimed at fitting data into pre-existing templates. NLG technologies of Enterprise-grade go beyond stating facts inside records.

Within data, they can express the most interesting and significant concepts and express them in consumable language, which is:

A. Relevant:

By knowing the meaning of what needs to be conveyed, recognize, and articulate the most salient observations.

B. Intuitive:

To create a natural, conversational language that describes complex concepts in an easy to consume way.

C. Timely:

Scaling data-driven communications that update the underlying information changes at any time.

7. What is the future of the Natural Language Generation?

Alexa, Cortana, and others are entering the age of intelligent personal assistants, helping to make daily activities simpler and more productive for customers. The enterprise is catching up, with conversational interfaces that make it easier for workers and consumers to engage, and is raising the bar on how these systems communicate.

Chatbots can help us gain more value from analytics.

Conversations with systems that have access to data on our environment will help us to understand the state of our jobs, our industries, our health, our homes, our communities, our computers, and our neighborhoods - all through the power of NLG. It's going to be the difference between receiving a report and having a discussion. The information remains the same, but the interaction would be more natural.

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