  Semantic similarity analysis and modeling is a fundamentally acclaimed task
in many pioneering applications of natural language processing today. Owing to
the sensation of sequential pattern recognition, many neural networks like RNNs
and LSTMs have achieved satisfactory results in semantic similarity modeling.
However, these solutions are considered inefficient due to their inability to
process information in a non-sequential manner, thus leading to the improper
extraction of context. Transformers function as the state-of-the-art
architecture due to their advantages like non-sequential data processing and
self-attention. In this paper, we perform semantic similarity analysis and
modeling on the U.S Patent Phrase to Phrase Matching Dataset using both
traditional and transformer-based techniques. We experiment upon four different
variants of the Decoding Enhanced BERT - DeBERTa and enhance its performance by
performing K-Fold Cross-Validation. The experimental results demonstrate our
methodology's enhanced performance compared to traditional techniques, with an
average Pearson correlation score of 0.79.
