Paraphrasing is about generating a new content that keeps the same sense as the original content, but with different words.
Performing simple paraphrasing by simply changing a couple of words is one thing, but generating advanced paraphrasing that completely changes the structure of sentences and the vocabulary used is another beast! Modern models like GPT-3 and GPT-J, now make it possible to easily create advanced and complex paraphrasing that properly keeps the main sense while using a different wording.
GPT-J is the most advanced open-source NLP model as of this writing, and this is the best GPT-3 alternative. This model is so big that it can adapt to many situations, and perfectly sounds like a human. For advanced use cases, it is possible to fine-tune GPT-J (train it with your own data), which is a great way perform paraphrasing that is perfectly tailored to your industry.
Marketing teams do appreciate paraphrasing as it makes their work much faster and less repetitive. Here are a couple of examples:
Creating marketing content can be long, tedious, and repetitive. It is sometimes interesting to get a hand from AI to increase productivity! Imagine you want to write a new blog article that partially says the same thing as another blog post your wrote earlier. You can paraphrase part of this content and supplement it with new original content.
Writing product descriptions is sometimes very repetitive. Sometimes products are very similar but you don't want to copy paste the same description. Using paraphrasing can really help.
If you are creating ads on a regular basis, you might sometimes lack inspiration. Paraphrasing is your friend here.
In order to make the most of GPT-J, it is crucial to have in mind the so-called few-shot learning technique: by giving only a couple of examples to the AI, it is possible to dramatically improve the relevancy of the results, without even training a dedicated AI.
Sometimes, few-shot learning is not enough (for example if your paraphrasing relies on very specific content, bound to your industry only). In that case, the best solution is to fine-tune (train) GPT-J with your own data.
Building an inference API for paraphrasing based on GPT-J is a necessary step as soon a you want to use paraphrasing in production. But building such an API is hard... First because you need to code the API (easy part) but also because you need to build a highly available, fast, and scalable infrastructure to serve your models behind the hood (hardest part). It is especially hard for machine learning models as they consume a lot of resources (memory, disk space, CPU, GPU...).
NLP Cloud proposes a paraphrasing API based on GPT-J that gives you the opportunity to perform paraphrasing out of the box, with breathtaking results. If the base GPT-J model is not enough, you can also fine-tune/train GPT-J on NLP Cloud and automatically deploy the new model to production with only one click.