The current applications of Natural Language Generation dominate in comparison to its vast potential in imparting human-like capabilities in AI systems.

FREMONT, CA – The natural language processing sector has burgeoned at an unprecedented rate over the last few years, more so than natural processing generation, which provides more significant challenges during implementation. Natural language processing (NLP)enables computers to understand human languages, whereas natural language generation (NLG) empowers computers to generate output understandable by humans.

The process of NLG is significantly more complicated when compared to NLP. Hence, the applications of NLG are vastly limited. However, the potential of NLG in terms of real-world applications far outweigh those of NLP. Thus, there is a need for continued investment and research in NLG.

NLG is similar to human speech and expression and requires sophisticated decision-making capabilities. It also needs to consider numerous rules, regulations, conventions, and constraints, including the amount of information that needs to be conveyed, the structure of the data, the flow of sentences, choice of words, referring expressions, syntax, and much more.

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Although NLG finds applications in several areas, its full potential is vastly unexplored. Currently, NLG is leveraged in business intelligence interpretation. The incorporation NLG has enabled enhanced analytics data-based report generation. The reports created through NLG empowered tools offer a better understanding of insights to business leaders. However, the NLG tools cannot derive insights from unstructured data and have to utilize data from structured databases.

Analytics dashboards can be significantly enhanced with the incorporation of NLG. Comprehensible presentations of actionable insights will enable business leaders to take quick and effective decisions, eliminating the need for poring over vast analytics reports and charts. Innovation in the NLG sector can also lead to artificial intelligence (AI) systems capable of developing technical content, including part and product descriptions, internal communications, agreements, contracts, and other textual processes.

NLG technology can significantly improve chatbots by imparting them with a better understanding of human language, thus facilitating human-like conversations. The context-sensitive chatbots can offer personalized user experiences, allowing businesses to automate their customer service verticals. They can be used for multiple purposes, including query resolution and virtual assistance.

The current applications of NLG are a mere scratch on the vast bubble of its potential that is yet to be reached. The research and innovation in this sector will inevitably lead to novel applications in various fields, thus boosting the capabilities of AI. 

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