AI paraphrasing has become so popular nowadays that writers use them every day for improving their own write-ups.
It is no secret that content writing is immensely popular and that content writers have tight deadlines due to huge workloads.
They use content optimization tools to improve their own productivity and spend less time on editing and post-processing. One of the most used types of content optimization tools is paraphrasing tools.
Today, we are going to explore a little bit of what happens behind the scenes in an AI paraphrasing tool.
More specifically, we are going to see what machine learning technologies are applied and how they drive these paraphrasing tools.
Process of AI Paraphrasing and Which ML technologies are Used in It
We are going to check out what is the process that happens when an AI paraphraser receives some input.
We are also going to look at which kind of ML technology is used during each step of the process. So, let’s start.
Text Detection and Analysis
This is the first step in the paraphrasing process. The software/online tool has to detect the text provided to it and analyze it.
During this analysis, the individual words are recognized, and the meaning of phrases and sentences is extracted.
Depending on the tool being used, tone detection also occurs during this phase.
So, how does all of this happen? Well, in this phase a subfield of machine learning called Natural Language Processing (NLP) is used.
NLP basically combines linguistics, computer science, and artificial intelligence.
With NLP, computer systems are able to understand and interact with natural language in a way similar to humans.
Understanding text with NLP involves the following steps:
- Tokenization. The text is divided into tokens, where each token is one character.
- The combination of tokens used to form words is analyzed to see if all words are grammatically correct or not.
- The combination of words to form sentences is checked if it is semantically correct and contextually okay.
This is where it ends if the purpose is just understanding. There are more steps involved if the task requires paraphrasing. So, let’s move on.
Paraphrasing
Now, paraphrasing text can be done in a fair few ways. But let’s see what are the two basic ways in which paraphrasing is done with AI tools. There are two steps involved in that.
A. Machine Learning—Classification
After understanding the text is over, an AI paraphraser will use machine learning to find out whether the important words and phrases have synonyms or not.
For that purpose, it will run those words/phrases through its own catalog of known words and pick out the ones that have the same meaning.
This is done via machine learning and more specifically it is a machine learning classification task. The tool classifies words according to their meaning. In machine learning, the system learns to find patterns among the given data.
Once it has learned to find these patterns, it can identify them in new and unknown datasets as well.
This is basically what happens during paraphrase identification—patterns where words having similar meanings are identified.
Then these synonyms are used for paraphrasing the input sentences and changing them syntactically, but not semantically.
Example of a Paraphrasing Tool Using This Technique
You can find a lot of paraphrase tools online that utilize this technique. We will show you an example in which we utilize an AI paraphrasing tool. You can see it below.
In this example, we can see that the different words have been replaced with synonyms that have the same meaning.
Another thing that we can see is that the new words are bold. Clicking on the bold words opens a drop-down list that contains even more synonyms.
This is possible because this paraphrase tool utilizes a machine-learning classification technique.
B. Machine Learning—Generation of Semantically Identical Text
In paraphrase generation, the classification approach is ditched in favor of the generation approach.
Basically, instead of finding words and phrases that have the same meaning and using them, it generates new sentences and phrases themselves.
There are multiple ways in which this can be done. A popular technique is to use a large language model (LLM) like GPT-4.
This is a pre-trained transformer that can create human-like text from prompts.
Naturally, it is very good at paraphrasing texts too. It is available as an API and many AI paraphrasers use it.
Other approaches that work are using syntactic trees, reinforcement learning, deep learning, and even the combination of several of these techniques.
These approaches are generally more time-consuming compared to using LLMs and pre-trained models.
Example of a Paraphrasing Tool Using This Technique
Nowadays you don’t have to find and use GPT-4 raw, instead, you can simply utilize some tools that have GTP-4.
Fortunately, the tool we discussed in our previous example also utilizes GPT-4 in some of its modes. To see an example of this, check out the image below.
You can see that entire phrases are completely changed. That’s possible because of the generation of semantically identical text with the help of large language models.
Conclusion
So, these are some of the machine learning technologies and techniques that are used in AI paraphrasing. Since there are different technologies and not all tools use the same technologies, differences in paraphrasing arise.
Hopefully, this article helped you to understand a little bit more about machine learning technologies used in AI paraphrasing. If you want to learn more about AI, then you can head to our blog and find more information.
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