Exploring the Challenges of Natural Language Generation

What are the challenges of Natural Language Generation?

Natural Language Generation (NLG) is the process of automatically producing natural language text from structured data. It has applications in many areas including natural language processing, knowledge representation, machine learning, and artificial intelligence. NLG can be used to create content for websites, chatbots, virtual assistants, and more. While NLG offers great potential, there are several challenges associated with its implementation.

Limited Access to Data

NLG systems require large amounts of data in order to accurately reproduce natural language. This data needs to be accurate, complete, and up-to-date. Unfortunately, access to relevant data can often be difficult or expensive, preventing some organizations from being able to take advantage of NLG technologies.

Complexity

NLG systems require complex algorithms to create natural language output. Developing these algorithms can be time consuming and costly. Additionally, NLG systems must constantly evolve to accommodate changes in language and context.

Uncertain Output Quality

NLG systems can produce unpredictable results. If a system is not properly trained and maintained, it can produce output that is incomprehensible or incorrect. As such, NLG systems must be regularly tested and evaluated to ensure that they produce quality output.