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Research Proposal on Text Sequence Generation with Deep Transfer Learning

Research Proposal on Text Sequence Generation with Deep Transfer Learning

  Text sequence generation is a class of text generation that generates a sequence of sentences to produce the abstract or outline for the entire document. Traditional deep learning methods for text sequence generation automatically extract the features and produce the sequence of sentences. However, traditional deep learning methods face challenges in understanding different domain knowledge to produce an accurate abstract which leads to the repeatability of a word in the sequences. In text sequence generation, it is necessary to recognize knowledge over various domains with appropriate key terms.

  Transfer learning is an approach of deep learning that utilizes knowledge transfer from one domain to another. The significance of transfer learning is less computational sources and less time due to its pre-learned knowledge from different domains. Deep transfer learning for text sequence generation uses a small amount of data to train the model and utilizes transferred knowledge to produce accurate abstracts with required key terms. Text sequence generation with a deep learning model generates a sequence of text with high quality and consistent content.

  • Natural Language Processing (NLP) diversely comprises several sequence generation tasks effectively performed with deep learning models.

  • Under the pre-trained data of transfer learning, it demands less memory and simple training requirements for NLP tasks.

  • Subsequently, the transfer learning framework is instigated for natural language generation tasks via large-scale pre-trained language models.

  • The pre-trained transfer learning model is the optimal scheme for sequence generation tasks and achieves outstanding performance.

  • Presently, transfer learning is enforced for multi-source sequence generation tasks by incorporating self-supervised pre-training and supervised generation.

  • Transfer learning for sequence generation is the steady fine-tuning technique that improves performance and generalizability toward multi-source sequence generation.