This article is about the computer processing ability. For the psychological concepts, see Origin of language or Universal grammar
Natural language generation (NLG) is a software process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems than can produce understandable texts in English or other human languages from some underlying non-linguistic representation of information".[1]
While it is widely agreed that the output of any NLG process is text, there is some disagreement about whether the inputs of an NLG system need to be non-linguistic.[2] Common applications of NLG methods include the production of various reports, for example weather [3] and patient reports;[4] image captions;[5] and chatbots.
Automated NLG can be compared to the process humans use when they turn ideas into writing or speech. Psycholinguists prefer the term language production for this process, which can also be described in mathematical terms, or modeled in a computer for psychological research. NLG systems can also be compared to translators of artificial computer languages, such as decompilers or transpilers, which also produce human-readable code generated from an intermediate representation. Human languages tend to be considerably more complex and allow for much more ambiguity and variety of expression than programming languages, which makes NLG more challenging.
NLG may be viewed as complementary to natural-language understanding (NLU): whereas in natural-language understanding, the system needs to disambiguate the input sentence to produce the machine representation language, in NLG the system needs to make decisions about how to put a representation into words. The practical considerations in building NLU vs. NLG systems are not symmetrical. NLU needs to deal with ambiguous or erroneous user input, whereas the ideas the system wants to express through NLG are generally known precisely. NLG needs to choose a specific, self-consistent textual representation from many potential representations, whereas NLU generally tries to produce a single, normalized representation of the idea expressed.[6]
NLG has existed since ELIZA was developed in the mid 1960s, but the methods were first used commercially in the 1990s.[7] NLG techniques range from simple template-based systems like a mail merge that generates form letters, to systems that have a complex understanding of human grammar. NLG can also be accomplished by training a statistical model using machine learning, typically on a large corpus of human-written texts.[8]
^Reiter, Ehud; Dale, Robert (March 1997). "Building applied natural language generation systems". Natural Language Engineering. 3 (1): 57–87. doi:10.1017/S1351324997001502. ISSN 1469-8110. S2CID 8460470.
^Gatt A, Krahmer E (2018). "Survey of the state of the art in natural language generation: Core tasks, applications and evaluation". Journal of Artificial Intelligence Research. 61 (61): 65–170. arXiv:1703.09902. doi:10.1613/jair.5477. S2CID 16946362.
^Cite error: The named reference fog was invoked but never defined (see the help page).
^Cite error: The named reference portet was invoked but never defined (see the help page).
^Farhadi A, Hejrati M, Sadeghi MA, Young P, Rashtchian C, Hockenmaier J, Forsyth D (2010-09-05). Every picture tells a story: Generating sentences from images(PDF). European conference on computer vision. Berlin, Heidelberg: Springer. pp. 15–29. doi:10.1007/978-3-642-15561-1_2.
^Cite error: The named reference Ehud was invoked but never defined (see the help page).
^Ehud Reiter (2021-03-21). History of NLG. Archived from the original on 2021-12-12.
^Perera R, Nand P (2017). "Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature". Computing and Informatics. 36 (1): 1–32. doi:10.4149/cai_2017_1_1. hdl:10292/10691.
and 25 Related for: Natural language generation information
Naturallanguagegeneration (NLG) is a software process that produces naturallanguage output. A widely-cited survey of NLG methods describes NLG as "the...
large language model (LLM) is a computational model notable for its ability to achieve general-purpose languagegeneration and other naturallanguage processing...
is provided as an overview of and topical guide to natural-language processing: natural-language processing – computer activity in which computers are...
a euphemism, e.g. "deepfakes for text"[citation needed] for natural-languagegeneration; "deepfakes for voices" for neural voice cloning, etc.) Significant...
AI model. A prompt is naturallanguage text describing the task that an AI should perform. A prompt for a text-to-text language model can be a query such...
Programming languages have been classified into several programming languagegenerations. Historically, this classification was used to indicate increasing...
Controlled naturallanguages (CNLs) are subsets of naturallanguages that are obtained by restricting the grammar and vocabulary in order to reduce or...
Referring expression generation (REG) is the subtask of naturallanguagegeneration (NLG) that received most scholarly attention. While NLG is concerned...
Narrative Science was a naturallanguagegeneration company based in Chicago, Illinois, that specialized in data storytelling. As of December 17, 2021...
derivation from the Penman Sentence Plan Language, they are thus continuing a long tradition in NaturalLanguageGeneration and this has been their original domain...
naturallanguage processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language...
Racter is an artificial intelligence program that generates English language prose at random. It was published by Mindscape for IBM PC compatibles in 1984...
Constraint Language OMG Available Specification Version 2.0, May 2006 Imran Sarwar Bajwa (October 2010). "OCL Constraints Generation from NaturalLanguage Specification...
the process of analyzing a string of symbols, either in naturallanguage, computer languages or data structures, conforming to the rules of a formal grammar...
parsing, coreference resolution, summarization, transliteration, naturallanguagegeneration and joint information extraction. Most of these works use an...
2020, Microsoft introduced its Turing NaturalLanguageGeneration (T-NLG), which was then the "largest language model ever published at 17 billion parameters...
of the companies in the space of automatic text generation, with a focus on NaturalLanguageGeneration (NLG). When it floated on London's Alternative...
analytics, facial and image recognition, machine learning, and naturallanguagegeneration. According to a white paper by software company Tupl Inc, continuous...
such as speech recognition, natural-language understanding, and natural-languagegeneration. Computer vision, dealing with how computers can understand and...
Generation Z (often shortened to Gen Z), colloquially known as Zoomers, is the demographic cohort succeeding Millennials and preceding Generation Alpha...
Automation platform that leverages naturallanguage understanding, naturallanguage processing, naturallanguagegeneration, deep learning and machine learning...