Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Role of Text to text Transfer Transformer in Data Augmentation
In this article we will learn about the role of Text to text Transfer Transformer technique in Data Augmentation and how we can use this technique to improve the NLP model.
In the current tech scenario Natural Language Processing has observed very rapid advancement in data augmentation field. Data Augmentation is used to improve the performance of model which is based on natural language processing (NLP). There are many techniques available using which we can achieve this in which one technique is Text to Text Transfer Transformer(T5) technique. We can use this technique for performing multiple NLP tasks by using unified text.
Data Augmentation
Data Augmentation is a technique which is used to train machine learning model based on various available data. We use data augmentation to reduce the overfitting, increase performance of the model and generate new points based on existing data. In natural language processing (NLP) data augmentation uses text preprocessing like back-translation, word replacement, paraphrasing and context modification.
Text to Text Transfer Transformer(T5)
Text to Text Transfer Transformer technique given by Google Research which feeds set of texts to the model which will give output in the text format. We use this approach to train the model on wide varieties of unlabeled data. T5 is trained on Text-to-Text technique where input and output will be in the form of text. The model is trained on large text data using unsupervised learning. T5 provide us various functionality like generating high quality textual output which is very useful in data augmentation.
Applications of Data Augmentation
Text to Text Transfer Transformer(T5) is used in various applications of Data Augmentation in NLP to increase efficiency and performance. Here are some applications −
Paraphrasing − We use T5 technique to generate alternative phrasings of sentences whose meaning will also be the same as the original sentence. Take an example of paraphrasing of the sentence "The cat has a rat in his mouth" which is paraphrased into "The rat is in the cat mouth".
Back-translation − Text to Text Transfer Transformer(T5) can be used to translate the sentence from one language to another language and then it can translate the sentence back to its original language. This back translation technique helps T5 to handle multilingual input. Take example of the sentence "The cat has a rat in his mouth" which T5 can translate into French language as "Le chat a un rat dans la bouche" and it can be translated back to "The cat has a rat in his mouth".
Sentiment Modification − We use T5 to modify the sentiment of the sentence and keep its original meaning as it is like we did in paraphrasing step. Take an example of "You are playing very well" and this can be transformed to "You are not playing very well” So this technique is used to create various sentimental based data.
Text Summarization − T5 can be used to summarize the sentence. Summarize means making long sentences shorter while preserving its original meaning. Consider the following sentence −
TutorialsPoint is an online platform that provides a wide range of tutorials and learning resources on various subjects, including programming, technology, and business. With a vast library of well-structured and easy-to-understand tutorials, TutorialsPoint caters to beginners as well as advanced learners, offering comprehensive knowledge in a user-friendly format. The platform also offers interactive coding exercises, quizzes, and practical examples, enabling learners to practice and apply their knowledge effectively.
Which can be summarized by T5 into
TutorialsPoint is an online learning platform, offering a vast range of well-structured tutorials, and practical examples to cater to learners of all types of learners.
Conclusion
In Natural Language Processing (NLP), data augmentation plays very important role in improving the performance and efficiency of model. Text-to-Text Transfer Transformer (T5) technique is a very powerful tool which has various range of functionalities. These functionalities used to train dataset and improve the model performance. There are many applications of T5 which includes text generation, transformation and summarization which helps to handle multilingual input and process variation in training the data. So, we can say Text-to-Text Transfer Transformer has overall improved the efficiency and effectiveness of the Data Augmentation field.
51 Views
