A Hybrid Natural Language Processing (HNLP) for Model Transformation
Banka Madhavi, Rama Chaithanya Tanguturi, S. Giri Babu · 2024
Model-to-model transformations involve converting one type of the model into another. This can be used in many ways for various reasons, considering model performance with particular deployment platforms. This paper introduces a Hybrid Natural Language Processing (HNLP) model to visualize model-to-model (M2M) transformation. The proposed HNLP combines Sub-word Embedding (FastText), Encoder-Decoder Architecture, and Attention-based Seq2Seq. The proposed approach focused on processing standard text into meaningful information. Subword Embeddings with FastText retain grammatical aspects by demonstrating words as n-grams, which enhances the interpretation of out-of-vocabulary phrases and unusual word structures. The Encoder-Decoder Architecture translates input sequences into symbolic representations that can be used for various NLP tasks. The Attention-based Seq2Seq method extends this approach by constantly focusing on vital regions of the input sequence during decoding, improving the model's capacity to provide accurate and contextually appropriate outputs. By combining these techniques, the HNLP model enhances sequence transformation quality and gives a more interpretable and visual path for understanding how input transformations occur between models. This visualization capacity is crucial for debugging, model improvement, and understanding the inner workings of complicated NLP systems. Experimental results show that the HNLP model performs well in various NLP tasks, demonstrating its promise as a reliable tool for practical applications and theoretical investigations into model-to-model transformations.