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1A Comprehensive Analysis of Various Tokenization Techniques and Sequence-to-Sequence Model in Natural Language Processing

Kuldeep Vayadande^1*, Ashutosh M. Kulkarni¹, Gitanjali Bhimrao Yadav¹, R. Kumar² and Aparna R. Sawant¹

¹Vishwakarma Institute of Technology, Pune, India

²VIT-AP University, Inavolu, Beside AP Secretariat, Amaravati AP, India

Abstract

This research paper provides an in-depth examination of various tokenization techniques and Sequence-to-Sequence (Seq2Seq) models, with an emphasis on the LSTM, Transformer, and Attention-based LSTM models. The process of tokenization, which breaks down text into smaller units, plays a vital role in natural language processing (NLP). This study evaluates different tokenization methods, including word-based, character-based, and sub-word-based methods. It also explores the latest advancements in Seq2Seq models, such as the LSTM, Transformer, and Attentionbased LSTM models, which have been successful in tasks like machine translation, text summarization, and dialog systems. The paper compares the performance of different tokenization techniques and Seq2Seq models on benchmark datasets. Additionally, it highlights the strengths and limitations of these models, which helps in understanding their suitability for various NLP applications. The aim of this study is to comprehensively understand the current advancements in tokenization and sequence-to-sequence modeling for NLP, particularly with regard to LSTM, Transformer, and Attention-based ...