Natural Language Generation, or NLG, is the computer process by which information that cannot be easily comprehended by humans is converted into a message that is optimized for human comprehension, as well as the name of the AI area dedicated to its research and development.
In computer science and AI, the phrase "natural
language" refers to what most people simply refer to as language, the
mechanism by which humans interact with one another and, increasingly, with
computers and robots.
The data processed by NLG technology is some sort of data,
such as scores and statistics from a sporting event, and the message created
from this data may take different forms (text or voice), such as a sports game
news broadcast.
The origins of NLG may be traced back to the mid-twentieth
century, when computers were first introduced.
Entering data into early computers and then deciphering the
results was complex, time-consuming, and needed highly specialized skills.
These difficulties with machine input and output were seen
by researchers and developers as communication issues.
Communication is also essential for gaining knowledge and
information, as well as exhibiting intelligence.
The answer suggested by researchers was to work toward
adapting human-machine communication to the most "natural" form of
communication, that is, people's own languages.
Natural Language Processing is concerned with how robots can
understand human language, while Natural Language Generation is concerned with
the creation of communications customized to people.
Some researchers in this field, like those working in
artificial intelligence, are interested in developing systems that generate
messages from data, while others are interested in studying the human process
of language and message formation.
NLG is a subfield of Computational Linguistics, as well as
being a branch of artificial intelligence.
The rapid expansion of NLG technologies has been facilitated
by the proliferation of technology for producing, collecting, and linking
enormous swaths of data, as well as advancements in processing power.
NLG has a wide range of applications in a variety of
sectors, including journalism and media.
Large international and national news organizations
throughout the globe have begun to use automated news-writing tools based on
NLG technology into their news production.
Journalists utilize the program in this context to create
informative reports from diverse datasets, such as lists of local crimes,
corporate earnings reports, and synopses of athletic events.
Companies and organizations may also utilize NLG systems to
create automated summaries of their own or external data.
Computational narrative and the development of automated
narrative generating systems that concentrate on the production of fictitious
stories and characters for use in media and entertainment, such as video games,
as well as education and learning, are two related areas of study.
NLG is likely to improve further in the future, allowing
future technologies to create more sophisticated and nuanced messages over a
wider range of convention texts.
NLG's development and use are still in their early stages,
thus it's unclear what the entire influence of NLG-based technologies will be
on people, organizations, industries, and society.
Current concerns include whether NLG technologies will have
a beneficial or detrimental impact on the workforce in the sectors where they
are being implemented, as well as the legal and ethical ramifications of having
computers rather than people generate factual and fiction.
There are also bigger philosophical questions around the
connection between communication, language usage, and how humans have defined
what it means to be human socially and culturally.
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You may also want to read more about Artificial Intelligence here.
See also:
Natural Language Processing and Speech Understanding; Turing Test; Workplace Automation.
References & Further Reading:
Guzman, Andrea L. 2018. “What Is Human-Machine Communication, Anyway?” In Human-Machine Communication: Rethinking Communication, Technology, and Ourselves, edited by Andrea L. Guzman, 1–28. New York: Peter Lang.
Lewis, Seth C., Andrea L. Guzman, and Thomas R. Schmidt. 2019. “Automation, Journalism, and Human-Machine Communication: Rethinking Roles and Relationships of Humans and Machines in News.” Digital Journalism 7, no. 4: 409–27.
Licklider, J. C. R. 1968. “The Computer as Communication Device.” In In Memoriam: J. C. R. Licklider, 1915–1990, edited by Robert W. Taylor, 21–41. Palo Alto, CA: Systems Research Center.
Marconi, Francesco, Alex Siegman, and Machine Journalist. 2017. The Future of Augmented Journalism: A Guide for Newsrooms in the Age of Smart Machines. New York: Associated Press. https://insights.ap.org/uploads/images/the-future-of-augmented-journalism_ap-report.pdf.
Paris, Cecile L., William R. Swartout, and William C. Mann, eds. 1991. Natural Language Generation in Artificial Intelligence and Computational Linguistics. Norwell, MA: Kluwer Academic Publishers.
Riedl, Mark. 2017. “Computational Narrative Intelligence: Past, Present, and Future.” Medium, October 25, 2017. https://medium.com/@mark_riedl/computational-narrative-intelligence-past-present-and-future-99e58cf25ffa.
