HIGH PERFORMANCE ARTICLE WRITING USING DEEP LEARNING

Content writers spend endless nights creating content which might or might not aid their requirement. A lot of times the content produced is not good in terms of qualitative nature of the entire text. Technical writing can be equally challenging The challenge of understanding topics, finding data specific to the need of topic, making sure information acquired is validated and finding time to do the research required can be cumbersome The model we propose aims to shorten the time it takes to create high-quality content. Traditional supervised state-of-the-art models, which were utilized in content generation, are based on a variety of assumptions from various research viewpoints but it produced unwanted data which lead to decelerating efficiency. Long strings of text were generated, but they lacked creativity and human sentiment, and they couldn’t tell the difference between good content and crude language. The content created was often repetitive in nature. Our project aims to help in generating content for technical articles which require factual information or just reference material for writers needing good quality data in a short amount of time. We use a transformer-decoder only model also known as GPT-2, which is an unsupervised language model. It showed that training on larger dataset and having more parameters improved the capability of language model to understand tasks and surpass the supervised language model. As a language model it produces large amounts of quality copy. Through our project content can be enhanced and produced faster, while humans can intervene to insert creativity and uniqueness. Deep learning and natural language processing are the two key features of artificial intelligence (AI) technologies that help AI tools in using prevailing information about any subject from the database and processing it accordingly.