The success of humans as a species owes to their ability to communicate and share information in the form of language. Around 7,100 languages are spoken all around the world. Information is exchanged through natural languages via emails, news, social media, advertisements, and so on. Irrespective of how humans share their thoughts, the recipient must understand what is being conveyed.
If sophisticated algorithms could be trained to speak like humans and respond to questions, the costs of meeting customer demands could be reduced. In the same way, business communication becomes streamlined when simple, natural language is used. This is where natural language generation (NLG) comes in.
NLG is a branch of artificial intelligence (AI) that generates language as an output based on the data provided as input. NLG systems are used for automated report generation, summarization, and dialogue generation.
According to Global Industry Analysts Inc., the global market for NLG is expected to significantly increase in the post-COVID scenario, rising from an estimated US$586.7 million in 2020 to US$1.4 billion by 2027, with a CAGR of 13.5%. In the United States, the NLG market is estimated to be US$159.9 million in 2020, while China is projected to reach a market size of US$314.5 million by 2027, with a CAGR of 18% over the analysis period 2020 to 2027.
In this article, you'll learn how NLG works, how it differs from NLP and its benefits.
What is NLG?
NLG is the technique by which a computer system can produce natural language text from structured information such as databases. In simple words, NLG aims to produce human-readable text from machine-understandable data.
NLG has many potential applications, including automated customer service agents, virtual personal assistants, summarizing complex information, and generating personalized advice.
How Does NLG Work?
NLG systems use structured data, such as a database, to generate human-readable, natural language text using various processes. These include:
Source of Data
The quality of the data source will determine how accurately the NLG engine can generate natural language output. Data sources should be validated and cleaned before they are used in an NLG system.
Data understanding is the process of understanding the data being used for NLG, including its structure, meaning, and potential implications. It involves activities such as data exploration, data cleaning, data integration, and data modeling.
Content determination is selecting the relevant content that needs to be included in an NLG output. This process is generally determined by the context of the situation and the user's intent.
Document Creation and Structuring
Document creation and structuring are automatically generating structured documents from input data. This involves taking data, such as text, images, or data from databases, and using natural language processing (NLP) techniques to generate a coherent, structured document.
Sentence aggregation generates coherent and meaningful sentences from smaller sentences or clauses. This step is often used in summarization, which helps condense large amounts of information into a concise summary.
Grammatical structuring involves using a set of rules to construct meaningful sentences from data that is input into an NLG system. It consists in parsing a given text into its components, such as nouns, verbs, and adjectives, and then using this information to create a grammatically correct sentence.
Presenting the Language
This is the final step wherein the NLG software will generate the output in the format specified by the user. This could be in the form of a customer-targeted email or an answer from a voice assistant.
What Algorithms are Used in NLG?
Some of the popular algorithms used in NLG are:
Recurrent Neural Networks (RNNs) are a type of artificial neural network with internal memory, which is used to process and interpret sequences of inputs. They are particularly useful for processing temporal data, such as speech, text, and time series data.
RNNs can generate an entire sentence or paragraph given a single prompt or a sequence of words that follow a given pattern. They are particularly well-suited to NLG tasks because they can "remember" past inputs and process them to generate new outputs.
XLNet is a transformer-based model that combines the advantages of the autoregressive (AR), and the autoencoder (AE) approaches to language modeling. It uses the permutation language modeling technique to capture the dependency between words in a sentence, and it also uses the bidirectional context between words.
XLNet can be used in text summarization, dialogue, and text generation. It can generate multiple sentences in a row, allowing for more complex and intricate text generation.
BERT (Bidirectional Encoder Representations from Transformers) is a deep learning algorithm based on NLP pre-training for text understanding. It is a technique for pre-training language representations, meaning it can create a contextual representation of words in a sentence.
BERT can generate natural language summaries by understanding the context of a given passage and then developing a summary based on essential information. It can also be used to answer questions by finding relevant text in the passage and then generating an answer based on those pieces.
LSTM (Long Short-Term Memory) is capable of learning long-term dependencies. It has a memory cell that can remember information for long periods and a gating mechanism that can control the flow of information.
LSTMs are trained on large datasets of natural language text and can be used to generate text similar to the original data. Additionally, LSTM predicts the next word or phrase in a sentence, given the previous words or phrases.
How is NLG different from NLP and NLU?
NLP is a branch of AI that deals with understanding and manipulating language. It is concerned with how computers understand and process natural language.
Natural Language Understanding (NLU) is a subset of NLP that involves machines understanding the meaning of language. NLG is also a subset of NLP but focuses on the generation of natural language by computers.
For example, a NLP chatbot might use keyword recognition to understand what a user is saying. It might then use NLU to understand the meaning of what the user is asking. Finally, NLG might be used to generate a response that is in natural language instead of a robotic response.
What are the Benefits of NLG?
Let's look at some of the benefits of using NLG systems:
Automated report generation
NLG can produce automated reports, including summarizing data and generating reports. Businesses can use this to generate meaningful insights from structured data quickly.
Efficiency in content generation
NLG can help increase content generation by automating the process of creating content from data. This can help businesses generate more content in less time and with fewer resources.
Eliminates human error
NLG systems can accurately generate reports, documents, and other written materials, ensuring that the output is free of typos, spelling, and other manmade errors.
NLG can reduce the time it usually takes to create a report or document. It eliminates manual data entry and formatting and automatically creates text based on the data provided.
Automated dialogue generation
NLG can generate dialogue for virtual agents, chatbots, and digital assistants. NLG-powered dialogue generation can provide organizations with a way to offer better customer service while increasing efficiency and reducing costs.
NLG has the potential to revolutionize how we interact with machines today. NLG generates human-like language output, which can save time and reduce errors in written communication. By using NLG, businesses and organizations can streamline their operations, improve their customer experience, and reduce costs. NLG technology is expected to become more advanced, potentially generating more complex and sophisticated language.
Cogent Infotech is a tech consulting company that leverages high-grade technology to solve complex problems for its clients. For more information on NLG, AI, Machine Learning, the Internet of Things (IoT), and similar cutting-edge technologies, please visit Cogent inCights.