What are NLU and NLG? Explaining their significance, differences, and application areas in natural language processing!

NLU (Natural Language Understanding) and NLG (Natural Language Generation) are technologies that play a vital role in improving the language and background comprehension of LLMs, which have attracted significant attention in recent years. By utilizing each within LLMs, it has become possible to analyze complex contexts and intentions, moving a step beyond conventional simple keyword recognition.

Regarding NLU and NLG, many people may have questions such as "What are the specific differences in their roles?" or "How can they be utilized in business?"

In this article, we will introduce the definitions and significance of the terms NLU and NLG in an easy-to-understand manner. Furthermore, we will explain their respective application fields, providing content that allows for a comprehensive understanding of the basic information regarding NLU and NLG.

1. What is NLU (Natural Language Understanding)?

NLU (Natural Language Understanding) is a technology used by computers to analyze the context and intent of human language. It understands data such as text and voice as meaningful information beyond mere strings of characters, reading the emotions (positive/negative, etc.) held by that data as well as the speaker's intent (questions, requests, commands, etc.).

NLU is positioned as an important sub-field of Natural Language Processing (NLP) and aims to accurately capture the meaning and intention behind the text.

Advancements in NLU Methods

Conventional NLU utilized rule-based methods through keyword matching or probability models such as the Hidden Markov Model (HMM) for analysis. These methods search for keywords within the input text and perform analysis based on those keywords, which had limits in the understanding of context and intent.

Consequently, deep learning models such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) have come into use in recent years. By utilizing deep learning models, language understanding that considers context has become possible, allowing for more high-precision comprehension of text meaning and intent.

In particular, utilizing pre-trained models based on the Transformer architecture, such as BERT and GPT, significantly improves context understanding and the capturing of fine nuances. This is because these models learn the importance of phrases and the influence of context within the text, allowing for more precise interpretation of intent.

Along with the development of deep learning models, AI has become capable of deeper understanding of human language through NLU, and the ability to extract meaning has improved.

Why is NLU Important?

The main significance of NLU is extracting essential meaning and intent from input natural language. Through NLU, AI can understand human language and respond more accurately and effectively.

It also has the following roles:

• Contextual Understanding and Situational Awareness: AI achieves more advanced understanding based on context and background information, such as proper interpretation of ambiguous expressions and polysemous words.

• Realization of Natural Dialogue: Supports natural conversation with users by determining appropriate responses and actions based on the extracted meaning and intent.

• Utilization of Unstructured Data: Contributes to data analysis by extracting useful information from unstructured data such as voice and video.

NLU is attracting attention as a foundation for more natural and efficient dialogue and data processing in modern AI systems.

Steps for AI to Execute NLU

In the following example, we introduce the flow of NLU execution when an AI chatbot receives the question "How is the weather tomorrow?"

Through the NLU processing described above, AI understands "what was said" and "what response is appropriate," and by converting it into a structured internal representation, it can execute appropriate responses or processes. Furthermore, because in Japanese the same word or expression can hold different meanings depending on context, NLU requires more appropriate interpretation.

To solve issues regarding Japanese interpretation, utilizing LLMs (Large Language Models) provided with high-quality data annotation is recommended.

2. What is NLG (Natural Language Generation)?

NLG (Natural Language Generation) is a technology where AI systems generate natural language sentences that are easy for humans to understand. It converts input data (numerical data, structured data, symbols, etc.) into sentences or voices that feel natural for humans to read or hear.

Conventional NLG generated sentences by embedding data according to pre-determined templates.

In recent years, methods of flexibly generating sentences based on input data using advanced LLMs such as the GPT series have become the mainstream. By utilizing LLMs, more natural linguistic expressions according to context are possible compared to before.

In NLG, the following deep learning models are mainly used:

• RNN: A technology for generating sentences based on time-series data or sequential information.

• LSTM: An advanced model of RNN, featuring a network capable of processing data with long-term dependencies.

• Transformer: An architecture capable of high-speed processing, demonstrating excellent performance especially in context understanding and processing large-scale data.

By utilizing deep learning models, AI can generate meaningful sentences from vast amounts of data, facilitating smoother communication between AI and humans.

Related Article: What is LLM? Explaining Mechanisms, Types, Benefits, Introduction Procedures, and Use Cases!

Why is NLG Important?

NLG holds the following roles:

• Data Interpretation and Transformation

• Automation of Communication

NLG receives massive data or statistical information as input, extracts important points from that information, and outputs them as readable sentences.

Since complex datasets and numerical information can be provided in various formats such as reports, emails, or Web content, the interpretation and transmission of data are significantly streamlined. Therefore, the time and effort required for organizing and explaining information can be reduced compared to before.

Automation of daily information provision, such as bank account information or market reports, is also possible. Humans no longer need to create sentences manually, allowing information provision to be carried out quickly and efficiently.

Because NLG clearly conveys the meaning of data and provides information in a format easy for users to understand, it plays an important role in a wide range of business scenes.

Mechanism of Sentence Generation in NLG

NLG generally performs sentence generation through the following steps:

For NLG to generate readable and accurate sentences that feel natural as natural language, accurate and well-organized high-quality data is indispensable. Specifically, it is important that data preprocessing, such as sufficient training data and annotation (labeling), is performed appropriately.

If the quantity, quality, or diversity of data is insufficient, the quality and reliability of generated sentences will be low.

3. Differences and Relationships Between NLU and NLG

NLU and NLG exist in a reciprocal relationship as parts of NLP. The following are their differences:

In other words, the major difference is that NLU emphasizes "interpretation" of input linguistic information, whereas NLG focuses on "generation" as output.

On the other hand, NLU and NLG are closely related and often function in coordination. In systems where humans and AI engage in dialogue (chatbots, smart speakers, etc.), both are indispensable elements.

In this way, NLU and NLG exist in a complementary relationship; NLU interprets input language, and NLG aims to improve generative ability by producing appropriate language based on NLU's interpretations.

4. Application Fields of NLU and NLG

NLU and NLG are each utilized in various NLP fields. Here, we introduce their respective application fields.

Sentiment Analysis (Mainly NLU)

Through NLU, user emotions and intentions can be extracted from massive amounts of reviews, SNS posts, and survey data, which can be used to improve marketing strategies and customer service. By understanding customer emotions through NLU, companies can respond in a way that aligns with the customer, leading to improved customer satisfaction.

Search Engines (Mainly NLU)

NLU is used to understand the intentions of a massive number of users and display more relevant search results. By accurately understanding the information users are seeking, search precision improves and the user experience is enhanced.

Automatic Generation of Product Descriptions (Mainly NLG)

NLG can automatically generate attractive product descriptions based on product specification information and functional features. NLG allows for efficient processing of large amounts of product information, providing customers with attractive and easy-to-understand info.

AI Chatbots and Virtual Assistants (NLU and NLG)

In AI chatbots and virtual assistants, NLU understands question intent, requested content, and even emotional nuances from the user's spoken or written language with high precision. It can also handle ambiguous expressions and context.

Meanwhile, NLG generates natural response sentences based on the understood content in question-answering systems. By responding with appropriate and natural language to the information sought by the user, smoother dialogue is realized.

By having AI chatbots and virtual assistants handle frequently asked questions and routine procedures/guidance completely automatically, operators can concentrate on more complex and highly individualized inquiries. This contributes to the reduction of labor costs, recruitment, and education costs for the entire support department.

Automatic Generation of Data Analysis Reports (NLU and NLG)

By utilizing NLU for data analysis reports, massive amounts of information can be organized efficiently, and information necessary for analysis, discussion, and decision-making can be extracted easily.

Important keywords, topics, opinions, and emotions (positive/negative) are automatically extracted, classified, and summarized from the vast "text data (unstructured data)" existing within and outside the company, such as customer reviews, SNS posts, open-ended survey responses, daily reports, and emails.

Meanwhile, NLG combines the text analysis results from NLU with structured data such as sales data to automatically generate easy-to-understand reports and summary articles where the main points are organized.

Since reports that organize the evidence and points of attention necessary for decision-making can be obtained automatically from vast amounts of information, rapid data-driven judgments that do not rely solely on intuition or experience become possible.

5. Summary

NLU is a technology that analyzes the context and meaning of human language, whereas NLG is a technology that generates human-like sentences based on meaning and information. Both play an important role in facilitating natural communication between AI and humans.

However, many precautions and risks exist for business utilization, such as communication errors associated with misrecognition or misinterpretation, and accuracy degradation associated with bias or noise in training data.

High-quality annotation is vital to suppress the risks held by NLU and NLG. If high-quality annotation can be performed, NLU and NLG can accurately understand meanings from provided data and interpret/generate appropriately.

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