Natural language generation (NLG) and natural language processing (NLP) are two branches of artificial intelligence that deal with human languages. They have different goals, methods, and applications, but they also overlap and complement each other. In this article, you will learn how they differ and how they work together.
NLG is the task of creating natural language texts or speech from non-linguistic data, such as numbers, graphs, or images. For example, NLG can produce weather reports from meteorological data, summaries from long documents, or captions from photos. NLG systems use various techniques, such as templates, rules, or neural networks, to generate coherent and fluent texts that convey the intended meaning and tone.
Natural Language Generation (NLG) is part of generative artificial intelligence and is different from Natural Language Processing (NLP). The inputs of NLG's consist of data that are numbers, images, and others that are not natural language, but the outputs are. Let's take an everyday example with the weather. There are different measurements of the weather including temperature, air quality, wind speed, humidity, precipitation among others. A NLG model would take in the numbers from the weather data and create natural language like "cold" or "warm" output to words and audio. NLG has the most value with endangered languages and populations that have lower literacy rates because sound generation doesn't require reading or writing.
I would beg to disagree with the characterization of NLG as deriving its input from non-linguistic data since these isn't a bright line between what is linguist vs. non-linguistic. For example, there are a number of Domain Specific Languages that mirror subsets of Natural Language — I call this Quasi Natural Language, in that it is restricted to recognizing constructs that conform to a tightly defined grammar which will generate a single unambiguous logical form when parsed unlike the rampant ambiguity inherent in natural language from which a canonical restatement in Natural Language can be generated to confirm that the linguistic input had been correctly understood by a system.
NLP is the task of understanding, analyzing, or manipulating natural language texts or speech. For example, NLP can perform sentiment analysis, machine translation, question answering, or speech recognition. NLP systems use various techniques, such as parsing, tagging, or embedding, to extract features, structures, or semantics from natural language data.
Natural Language Processing (NLP)'s inputs and outputs are different from models with natural language generation. The inputs of these algorithms include natural language, words, sounds, audio, or similar data. The outputs are often numerical data that can include word counts, frequencies, sentiment, or question answering. Python is a programming language that does well with natural language processing because of its interpreted behavior. Put another way, it's able to process tricky things in language data such as the differences between the character and the number 3 without extra setup.
NLG and NLP differ in the direction of their tasks: NLG goes from non-linguistic data to natural language, while NLP goes from natural language to non-linguistic data. They also differ in the complexity and diversity of their inputs and outputs: NLG has to deal with heterogeneous and structured data sources, while NLP has to deal with ambiguous and unstructured natural language data. Moreover, they differ in the evaluation and quality of their results: NLG has to consider the readability, relevance, and originality of the generated texts, while NLP has to consider the accuracy, robustness, and scalability of the processed texts.
NLG and NLP overlap in some aspects, such as the use of linguistic knowledge, the application of machine learning, and the integration of multimodal data. For instance, NLG systems may use NLP techniques to analyze the input data, to select the appropriate words and phrases, or to revise the generated texts. Similarly, NLP systems may use NLG techniques to generate natural language responses, summaries, or explanations. Furthermore, both NLG and NLP systems may involve multimodal data, such as text, speech, images, or video, and require cross-modal understanding and generation.
NLG and NLP complement each other in many ways, such as enhancing communication, improving accessibility, and enabling creativity. For example, NLG systems can help users communicate better with data, by providing natural language summaries, visualizations, or narratives. NLP systems can help users communicate better with machines, by enabling natural language interfaces, dialogues, or feedback. Additionally, NLG systems can improve the accessibility of information, by producing natural language texts or speech for different audiences, languages, or devices. NLP systems can improve the accessibility of services, by providing natural language understanding, translation, or transcription. Moreover, NLG systems can enable creativity, by generating novel texts, stories, or poems. NLP systems can enable creativity, by analyzing, editing, or evaluating natural language texts.
Natural Language Generation (NLG) and Natural Language Processing (NLP) systems are becoming increasingly popular. For example, GPT-3 is a powerful neural network that can generate natural language texts on various topics and tasks, such as writing essays, composing emails, or creating headlines. Google Translate is a popular service that uses neural machine translation techniques to translate natural language texts or speech between different languages. Watson is a cognitive system that can answer natural language questions on various domains, such as medicine, finance, or trivia, using natural language understanding and reasoning techniques. Siri is a virtual assistant that can interact with users through natural language speech using speech recognition, natural language generation, and dialogue management techniques.
Most of these examples are bias toward neural techniques rather than traditional symbolic AI approaches which are no longer in vogue, however with modern hardware and the ability to tap into datasets like WordNet, we may be able to achieve more robust solutions that aren't subject to the risk of "hallucinations" that plague some large language model neural based approaches.