“People have always communicated through stories and language, why should we expect them to change now?”
– Harvard Business Review
Here are answers to the top five questions regarding natural language generation (NLG).
1) What is Natural Language Generation?
NLG, a subfield of artificial intelligence (AI), is a software process that automatically transforms data into plain-English content. The technology can actually tell a story – exactly like that of a human analyst – by writing the sentences and paragraphs for you. NLG is one of the fastest growing technologies being adopted in the enterprise. There many use-cases for NLG, but where it is seen to be most effective is when deployed to automate time-intensive data analysis and reporting activities.
2) What’s the goal of Natural Language Generation?
People have always communicated ideas from data. However, with the explosion of data that needs to be analyzed and interpreted, coupled with increasing pressures to reduce costs and meet customer demands, the enterprise must find innovative ways to keep up.
As it turns out, a machine can communicate ideas from data at extraordinary scale and accuracy. And it can do it in a particularly articulate manner. When a machine automates the more routine analysis and communication tasks, productivity increases and employees can focus on more high-value activities.
Per the Forbes article, “Why Big Data Needs Natural Language Generation to Work”:
“For many applications, natural language can be preferable to the engaging visual interfaces we often encounter. As attractive as visually rich dashboards can be, when it comes to information density, they are usually far inferior to language. In a paragraph and a few bullet points, we can quickly tell a rich and complex story…
But the bigger game of NLG is not about the language but about handling the growing number of insights that are being produced by big data through automated forms of analysis. If your idea of big data is that you have a data scientist doing some sort of analysis and then presenting it through a dashboard, you are thinking far too small. “
3) How is NLG different than NLP?
Gartner’s recent Hype Cycle for BI and Analytics sums up the difference between NLG and NLP (Natural Language Processing) well:
“Whereas NLP is focused on deriving analytic insights from textual data, NLG is used to synthesize textual content by combining analytic output with contextualized narratives.”
In other words, NLP reads while NLG writes. NLP systems look at language and figure out what ideas are being communicated. NLG systems start with a set of ideas locked in data and turn them into language that, in turn, communicates them.
4) What are the different variations of Natural Language Generation?
Per the Forbes article mentioned, earlier,
“The problem with most NLG platforms is that they hard code intelligence into a template. This makes for systems that are brittle and hard to change and are not able to accept new data without new coding.”
Templated NLG systems work well for applications that require the straightforward translation of data into text. However, for those looking to communicate data-driven information in a scalable fashion, it is necessary to utilize intelligent NLG systems that perform more than rules-based functions aimed to fit data into pre-existing templates. Enterprise-grade NLG technologies go beyond stating facts within data. They are able to express the most interesting and important concepts within data and express them in consumable language that is:
- Relevant: Identifies and articulates the most salient insights by understanding the context of what needs to be communicated.
- Intuitive: Generating natural, conversational language that explains complex concepts in a way that is easy to consume.
- Timely: Scaling data-driven communications that update anytime the underlying data changes.
5) What is the future of Natural Language Generation?
Alexa, Cortana and others are ushering in the era of intelligent personal assistants, helping to make everyday tasks easy and efficient for consumers. The enterprise is catching up, with conversational interfaces that are facilitating engagement across employees and to customers, raising the bar on how these systems communicate.
Per a recent article in the Harvard Business Review, “Bots that Can Talk Will Help Us Get More Value from Analytics”:
“Conversations with systems that have access to data about our world will allow us to understand the status of our jobs, our businesses, our health, our homes, our families, our devices, and our neighborhoods — all through the power of NLG. It will be the difference between getting a report and having a conversation. The information is the same but the interaction will be more natural.”