Natural Language Generation (NLG) Task

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A Natural Language Generation (NLG) Task is a natural language processing task that produces natural language expressions.



References

2021

  • (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Natural-language_generation Retrieved:2021-2-20.
    • Natural-language generation (NLG) is a software process that transforms structured data into natural language. It can be used to produce long form content for organizations to automate custom reports, as well as produce custom content for a web or mobile application. It can also be used to generate short blurbs of text in interactive conversations (a chatbot) which might even be read out by a text-to-speech system.

      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 the opposite of 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 concept 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.[1]

      NLG has existed since ELIZA was developed in the mid 1960s, but commercial NLG technology has only recentlybecome widely available. 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.

  1. Dale, Robert; Reiter, Ehud (2000). Building natural language generation systems. Cambridge, U.K.: Cambridge University Press. ISBN 978-0-521-02451-8.

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