Natural Language Generation

- Overview 

Natural Language Generation (NLG) is a software process driven by artificial intelligence (AI) that produces natural written or spoken language from structured and unstructured data. It helps computers to feed back to users in human language that they can comprehend, rather than in a way a computer might.

NLG uses machine learning (ML) and deep learning (DL) models to convert numbers into natural language text or speech. In its essence, it automatically generates narratives that describe, summarize or explain input structured data in a human-like manner at the speed of thousands of pages per second. 

However, while NLG software can write, it can’t read. The part of Natural Language Processing (NLP) that reads human language and turns its unstructured data into structured data understandable to computers is called Natural Language Understanding (NLU). 

NLG helps computers to communicate with users in a way that humans can understand, like generating reports, summaries, or responses in a conversational way, often used in chatbots and voice assistants.

- Key Features about NLG

• Function: Takes data as input and transforms it into human-readable text or speech.
• Application: Used in scenarios where a computer needs to communicate with humans in a natural way, such as generating customer service responses, summarizing complex data, or creating news articles.
• Part of NLP: NLG is a subfield of Natural Language Processing (NLP) which also includes Natural Language Understanding (NLU).

- How NLG Works

NLG can complete tasks such as: Language translation, Question answering, Converting unstructured data into a structured format.

NLG techniques include: 

• Aggregation: Merging similar sentences to improve readability and naturalness.
• Grammatical structuring: Applying grammatical rules to generate natural-sounding text.

• Data analysis: The system first analyzes the input data, identifying key information and relationships.
• Content selection: Determines which information to include in the generated text based on the context and purpose.
• Language generation: Constructs sentences using grammatical rules and appropriate vocabulary to form a coherent narrative.

- Examples of NLG in Practice

• Chatbots: Generating responses to user queries in a natural conversational style
• Voice assistants: Providing spoken summaries of information like weather updates or calendar events
• Financial reporting: Automatically creating financial reports based on numerical data
• Personalized content: Tailoring marketing messages or product descriptions to individual users

For example, NLG can be used after analyzing customer input (such as commands to voice assistants, queries to chatbots, calls to help centers or feedback on survey forms) to respond in a personalized, easily-understood way. This makes human-seeming responses from voice assistants and chatbots possible. 

It can also be used for transforming numerical data input and other complex data into reports that we can easily understand. For example, NLG might be used to generate financial reports or weather updates automatically.

[More to come ...]