What is Natural Language Generation (NLG)

Natural Language Generation (NLG) is the subfield of artificial intelligence that deals with the production of natural language from structured or unstructured data. In other words, NLG is the process of transforming data into natural written or spoken language using computer software. NLG enhances the interactions between humans and machines, automates content creation and distills complex information in understandable ways. In this article, we will explain what NLG is, how it works, what are its applications and challenges, and what are some examples of NLG systems.

What is NLG?

NLG is a subset of natural language processing (NLP), which is the broader field of artificial intelligence that enables computers to understand and generate human language. NLP encompasses both natural language understanding (NLU) and natural language generation (NLG). NLU is the process of analyzing and extracting meaning from natural language input, such as text or speech. NLG is the process of creating natural language output from some underlying representation of information, such as structured data or knowledge graphs.

NLG can be seen as the opposite of NLU: whereas NLU converts natural language into a machine-readable format, NLG converts a machine-readable format into natural language. However, NLG and NLU are not symmetrical in terms of their challenges and applications. NLU needs to deal with ambiguous or erroneous user input, whereas NLG needs to make decisions about how to express the intended meaning in a clear, coherent and engaging way. NLU generally aims to produce a single, normalized representation of the input, whereas NLG needs to choose from many possible ways of generating the output.

How does NLG work?

NLG systems can use different methods and architectures to generate natural language from data. However, a common approach is to follow a pipeline that consists of three main stages: content determination, text planning and surface realization.

• Content determination: This stage involves selecting and organizing the information that will be conveyed in the output. The input data can be structured (such as tables, graphs or databases) or unstructured (such as images, videos or documents). The output can be a single sentence, a paragraph or a longer document. The system needs to decide what information is relevant, important and interesting for the target audience and purpose.
• Text planning: This stage involves structuring and ordering the selected information into a coherent text. The system needs to decide how to divide the information into sentences and paragraphs, how to connect them with discourse markers (such as conjunctions, pronouns or adverbs), and how to use rhetorical devices (such as summaries, comparisons or contrasts) to enhance the readability and persuasiveness of the text.
• Surface realization: This stage involves generating the actual words and phrases that will form the output text. The system needs to decide how to lexicalize (choose words for) the concepts and relations in the input data, how to inflect (modify words for) grammatical features such as number, gender or tense, and how to punctuate and format the text.

These stages can be implemented using different techniques, such as rule-based systems, template-based systems or neural network-based systems. Rule-based systems use predefined rules and grammars to generate text from data. Template-based systems use predefined templates or slots that are filled with data values. Neural network-based systems use machine learning models that are trained on large corpora of human-written texts to learn how to generate text from data.

What are the applications of NLG?

NLG has many applications in various domains and industries, such as:

• Business intelligence: NLG can help businesses analyze and communicate complex data in simple language. For example, NLG can generate reports, summaries or insights from sales data, customer feedback or market trends.
• Education: NLG can help educators create personalized learning materials and feedback for students. For example, NLG can generate questions, exercises or explanations based on students’ performance, preferences or goals.
• Healthcare: NLG can help healthcare professionals provide better care for patients. For example, NLG can generate patient reports, diagnosis summaries or treatment plans from medical records, test results or clinical guidelines.
• Journalism: NLG can help journalists produce high-quality content faster and cheaper. For example, NLG can generate news articles, headlines or captions from data sources such as sports scores, weather forecasts or financial statements.
• Entertainment: NLG can help entertainers create engaging content for audiences. For example, NLG can generate poems, stories, songs or jokes from prompts, keywords or themes.

What are the challenges of NLG?

NLG is not a solved problem and still faces many challenges, such as:

• Data quality: The quality of the output depends on the quality of the input data. If the data is incomplete, inaccurate or inconsistent, the generated text may be misleading, incorrect or nonsensical.
• Evaluation: It is hard to measure the quality of the generated text objectively and automatically. Different criteria such as accuracy, fluency, coherence or relevance may be used to evaluate different aspects of the text. However, these criteria may not capture the subjective and contextual factors that affect the perception and satisfaction of the users.
• Ethics: The ethical implications of NLG are not fully understood and regulated. NLG can be used for good or evil purposes, such as informing or deceiving, educating or manipulating, empowering or exploiting. NLG can also raise issues such as privacy, accountability or bias.

What are some examples of NLG systems?

There are many examples of NLG systems that are available online or in the market, such as:

• GPT-3: GPT-3 is a neural network-based system that can generate natural language from any text input. It is one of the most advanced and versatile NLG systems in the world. It can perform various tasks such as answering questions, writing essays, creating summaries, generating code, composing emails and more¹.
• Wordsmith: Wordsmith is a template-based system that can generate natural language from structured data. It is one of the most widely used and scalable NLG systems in the world. It can produce various types of content such as reports, summaries, insights, narratives and more².
• Quill: Quill is a rule-based system that can generate natural language from structured data. It is one of the most intelligent and customizable NLG systems in the world. It can produce various types of content such as reports, summaries, insights, narratives and more³.

Conclusion

NLG is the subfield of artificial intelligence that deals with the production of natural language from structured or unstructured data. NLG has many applications and benefits in various domains and industries, but also faces many challenges and risks. NLG is an active and evolving research area that promises to revolutionize the way we communicate with data and machines.