  One of the principal objectives of Natural Language Processing (NLP) is to
generate meaningful representations from text. Improving the informativeness of
the representations has led to a tremendous rise in the dimensionality and the
memory footprint. It leads to a cascading effect amplifying the complexity of
the downstream model by increasing its parameters. The available techniques
cannot be applied to cross-modal applications such as text-to-image. To
ameliorate these issues, a novel Text-to-Image methodology for generating
fixed-length representations through a self-supervised Variational Auto-Encoder
(VAE) for semantic evaluation applying transformers (TexIm FAST) has been
proposed in this paper. The pictorial representations allow oblivious inference
while retaining the linguistic intricacies, and are potent in cross-modal
applications. TexIm FAST deals with variable-length sequences and generates
fixed-length representations with over 75% reduced memory footprint. It
enhances the efficiency of the models for downstream tasks by reducing its
parameters. The efficacy of TexIm FAST has been extensively analyzed for the
task of Semantic Textual Similarity (STS) upon the MSRPC, CNN/ Daily Mail, and
XSum data-sets. The results demonstrate 6% improvement in accuracy compared to
the baseline and showcase its exceptional ability to compare disparate length
sequences such as a text with its summary.
