Developing a Transformer-based Autoencoder Model for Sentence Embedding

In natural language processing (NLP), sentence embedding plays a key role in converting sentences into fixed-length vectors or numerical representations. These embeddings capture the relationships between words and the overall meaning of a sentence. It enhances machine understanding of language. Traditional sentence embedding methods often rely on feature-based techniques and demand substantial amounts of labeled data. These huge amount of data are expensive and challenging to obtain. To overcome this issue, this paper introduces a method that utilizes unsupervised techniques to generate sentence embeddings from unlabeled data. The proposed approach combines transformers and autoencoder to create more meaningful sentence representations. Autoencoders are used to compress data and map it into a latent space. On the other hand, transformers focus on capturing the contextual meaning of words within sentences. This hybrid model provides a more comprehensive way of representing sentence semantics.