Cloud Hosting

Paraphrasing tools help bloggers and writers in creating new content from the preexisting content. These tools use the advanced technology of artificial intelligence and machine learning to generate paraphrases. In this article, we will be discussing how artificial intelligence and machine learning are trained to work in a paraphrasing tool. Firstly, lets discuss the components of the paraphrasing task

There are two different tasks in paraphrasing. These two tasks are paraphrase identification (PI) and paraphrase generation (PG).

The purpose of the paraphrase identification task is to check if a sentence pair is pointing towards the same meaning. In paraphrase identification, the system yields a figure between 1 and 0. Here, the value 1 shows that the sentence pair have the same meaning while 0 exhibits that the sentence pair is not a paraphrase of each other. Github: https://github.com/nelson-liu/paraphrase-id-tensorflow.git

The paraphrase identification task is a machine learning task. The systems are always trained with a corpus of sentence pairs. The learned knowledge is then used by machine learning to identify if a sentence pair is paraphrased or not. First, the system is trained with a corpus of labeled sentences pairs. Then, use the learned knowledge to identify whether two sentences are paraphrases

In the second task of paraphrase generation, the aim is to generate one or more paraphrases of the input text automatically. So, the aim is to create paraphrases that are fluent and have the same meaning. The paraphrase identification system takes this task as a classification task whereas the paraphrase generation system takes this task as a language generation task. The algorithms of machine learning (ML) and artificial intelligence (AI) bring about the classification of sentences. It means that these algorithms create a model that is useful for input and output mapping. In other words, machine learning or ML uses a number of strategies to make two sentences similar in meaning.

As the focus of this article is paraphrasing or paraphrase generation, we will now look at different techniques of paraphrase generation. We can classify such techniques into two major categories.

In this approach, the paraphrase generation is controlled by a template or syntactic tree. Kumar and Ahuja with their associates proposed an approach in 2020 for paraphrase generation. It uses both syntactic trees and tree encoders by employing LSTM (long short-term memory) neural networks.

Another approach (retriever editor approach) was given in the same year for paraphrase generation in which embedding distance related to the source is used to select a similar source-target pair. After that, the editor has to modify the input sentence with the help of a transformer. The retriever should select the source-target pair with the highest similarity on the basis of embedding distance with the source. The job of the editor is to modify the input accordingly.

In these techniques, language models with fine-tuning such as GPT2 and GPT3 works to generate paraphrases. There is an approach of paraphrase generation using GPT2 where the ability of GPT2 to understand the language is exploited. GPT2 is trained on a large open-domain corpus, therefore, its ability to understand language is exceptional. The aim of this approach is to fine-tune the weight of the pre-trained GPT2 model.

In this section, we will discuss a unified system architecture that is capable of both PI and PG. The major components of such a system are as follows.

The first component of a system is to collect data from a variety of sources. The sources may be Quora duplicate question pairs, MSRP or Microsoft paraphrase research database, PARANMT 50M, etc. The training set is usually very large because these sources contain a lot of datasets with many thousands of sentence pairs. These different types of data are valuable to train the paraphrasing tool models.

The purpose of this stem is to increase data diversity. It is achieved by sampling and filtering the original data. Usually, paraphrase generation models give correct paraphrases with no recurrence. It is due to the huge lexical resource and syntactic diversity that is present in the data used during training. As a result, the paraphrasing tools generate various paraphrases having the same meaning, however, the vocabulary is varied. In addition, it is necessary to perform a number of transformations to the training data to enhance data diversity. As a result of this step, the diversity semantic similarity and fluency are provided to the system.

The system is trained so that it can perform the task of paraphrase generation. For this purpose, the Text-To-Text Transfer Transformer is used to train the system on data. For instance, it is possible to use T5 based pre-trained model for this purpose which is a Text-To-Text Transfer Transformer.

The models such as the T5 model have a self-attention technique that is used in transformers receiving input sequences and generating an output sequence. The output sequence is of the same length as that of the input sequence. So, it is important to compute every element of the output sequence by performing calculations of an average of the input sequence given.

In the end, the whole model is trained for up to 200 epochs on systems having at least 120GB of RAM (random access memory). The algorithm takes quite a lot of time, about three days, to train on the task of paraphrase generation. The system should be efficient as well as lightweight. It is possible to optimize the parameters to improve the performance of the system further.

There are many paraphrasing tools trained with Machine Learning and Artificial Intelligence.

For example: Paraphrasingtool.ai is the perfect example of AI Based paraphrasing tool that uses its own trained model using transformers to rewrite content. This paraphrasing tool is the most accurate, reliable, free and plagiarism free paraphrasing tool available on the web. It can rewrite content in any language automatically and accordingly. This paraphrase tool is very carefully tested to avoid any manual processing and to ensure the quality.

The process of paraphrasing has two tasks paraphrase generation and paraphrase identification. These tasks have huge significance in NLP or natural language processing. There are different approaches available for paraphrase generation. Artificial intelligence and machine learning play their roles in paraphrase generation and identification tasks.

Various models such as T5 model works for sentence generation. The system developed as a result of these algorithms is trained extensively with various datasets and data sources. Consequently, the paraphrasing tools based on artificial intelligence and machine learning have increased diversity and a huge vocabulary.

Link:
How AI and Machine Learning trained to work in Paraphrasing tool - Techiexpert.com - Techiexpert.com