Enhancing Pragmatic Nuance Decoding in Bidirectional Encoder Representation from Transformer
Johnwendy Chinedu Nwaukwa, Imianvan Anthony Agboizebeta · 2024
In the dynamic realm of linguistics and Natural Language Processing (NLP), this research tackles the challenge of decoding pragmatic nuances in semi-open-ended deterministic text, with a focus on BERT. Despite Large Language Models (LLMs) impressive capabilities, LLMs struggle with the intricate subtleties of pragmatic language. This study proposes a comprehensive framework, integrating N-Gram, POS Tagging, Sentence dependency parser and advanced techniques such as Bidirectional Encoder Representation from Transformer (BERT), to enhance pragmatic understanding and sentence rephrasing. Through meticulous data preprocessing, feature extraction, and model evaluation, the research demonstrates the proposed model's proficiency, surpassing traditional baselines. Real-world case studies underscore its practical utility. Results reveal the model's 82% accuracy in pragmatic understanding, showcasing effectiveness in real-world scenarios and positioning it as a robust solution for nuanced language processing. This work contributes to advancing NLP techniques, providing valuable insights for future investigations into domain-specific fine-tuning and larger pre-trained language models.