Online Paraphrasing Tools – A Look Behind These Tech Marvels

Paraphrasing tools are a convenient academic utility available online. Capable of transforming, reshaping, and molding any content, these tools help students rehash & modify write-ups, polish content and avoid plagiarism. Online academic writing and professional proofreading services offer some of the best paraphrasing tools online. Several of them are powered by the latest AI techniques, like deep learning, neural networks and Natural Language Processing.

Let’s take a closer look at two of the most powerful paraphrasing techniques used by the best paraphrasing tools for paraphrasing.

Paraphrasing Using NLP

Paraphrase generation is a prominent application in natural language processing. Utilized for content creation, machine translation, data augmentation and content recycling. Several academic & research institutions and renowned writing & proofreading service have native R&D teams, and NLP is one of the core techniques used in designing online paraphrase generators.

One powerful technique involved the creation of large language models using a neural network. These models can paraphrase extensive sentences and do so at both the sentence & macro level. Large language models are capable of providing a high quality of paraphrased results.

Large language models follow the encoder-decoder structure of the transformer architecture. Transformers in NLP are a system that can capture the relationships between different terms, clauses and referring expressions in a natural language text. GPT-2 and BERT models are some such robust transformer architecture employed by leading AI-powered paraphrasing tools.

Deep Learning and Paraphrasing

Deep learning techniques have been recently found to be highly effective in generating the text of data. Recurrent neural networks have been using deep learning techniques to generate text from dialogue speech acts. These models take both input and output a sequence of data to create models using Long Short Term Memory Model.

These modes take in data in a structured tree or graph format. Paraphrasing generators using the LSTM models determine the constraints necessary to shape the output content’s structure from the input data itself. The shape of the input data is a significant factor in determining the shape of the output data.

For example,

Input: John was born in London. He worked as a teacher.

Output: John was born in London and worked as a teacher.

John = A à London (Birthplace) and A à Teacher (Occupation)

The input data is converted into a chaining structure. John, a referring expression, is linked to two other significant expressions, London and teacher, through two specific relationships; the first one is the birthplace while the second one is occupation. LSTM can take note of these structural constraints to deliver better output data.

The chaining structure allows the model to infer & interpret different kinds of structural constraints from the input data itself.

And that’s all the space we have for today. If the information above intrigues you, check out white papers on Natural Language Processing and the latest deep learning models that allow machines to interpret natural language.

If you are looking to avail an authentic paraphrasing tool, then always choose the ones from a reputable academic writing business or genuine proofreading services online.


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