TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformers

TL;DR

TexIm FAST introduces a novel text-to-image representation method using transformers and a variational auto-encoder, significantly reducing memory usage and improving semantic similarity evaluation across diverse datasets.

Contribution

The paper presents a new cross-modal representation technique that generates fixed-length, pictorial embeddings for text, enhancing efficiency and accuracy in semantic similarity tasks.

Findings

Achieves over 75% reduction in memory footprint.

Improves semantic textual similarity accuracy by 6%.

Effectively compares sequences of varying lengths.

Abstract

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.