por Natural language generation, also called Natural Language Generation or NLG, is a process of generating coherent sentences in the form of natural language. Its objective is to generate text, but not randomly, since if that were the case, we could take vocabulary from any corpus we have been looking at and start adding random words without any kind of meaning.
This would also be natural language generation, because we would be generating text, but, as we will see later, NLG is not just generating words or phrases, but All the sentences that we make in the generation of natural language have to be coherent and must have cohesion.
Natural language generation algorithms
Natural language generation algorithms can write, but not read. This means that they do not understand what they are putting, since they do not have this skill. Hence the importance of paying attention to what they are trying to put in order to generate coherent text.
To write text, you must first understand it. Thus, it is necessary to convert unstructured data into something structured that is understandable by natural language generation algorithms. Like almost all datasets, initially the data comes in an unstructured form, because it comes with noise, punctuation marks and other aspects that do not provide any value.
These natural language generation algorithms must also be filtered, but they are stronger when processing them and, normally, are not as sensitive as a traditional master algorithm.
In this case, furthermore, processing is much lighter by avoiding the elimination of stopwords. If, for example, in NLG we eliminate stopwords, the algorithm we obtain will generate combinations of incomplete words with very basic grammar, and that is precisely what we do not want and are avoiding, since what we are looking for is for the natural language generation algorithm to speak in the most natural way possible. So that, In NLG problems it is necessary that we maintain the entire vocabulary; What we can and should delete are punctuation marks.
NLU or Natural Language Understanding
As we have said, it is not only important that we generate language, but also make it understandable. In NLP (natural language processing), this is related to another area called NLU or Natural Language Understanding.
NLG + NLU + NLG
Let’s see a small diagram that clearly shows us how each of the three areas would be formed: the natural language processinghe natural language understanding and the natural language generation:
The area that encompasses everything is NLP.
The NLU, for its part, allows us to understand, among other things, the way in which language arrives and is processed.
On the opposite side we have the NLG, which corresponds to tasks much more focused on text generation, such as generation of fakenewssummaries, weather report…
We also have some sections that are in the middle of the NLG and the NLU, such as conversational agents, chatbots and all these types of conversation systems that exist. and that, in fact, are currently very fashionable.
These systems, in addition to generating language, have to understand the person they are speaking to, who is generally a human.
Whats Next?
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