Paraphrase Identification

Paraphrase identification is a fundamental task in Natural Language Processing (NLP) that aims to determine whether two sentences express the same meaning using different wording. This classification-based project explores various deep learning architectures to classify sentence pairs into two categories: paraphrase or non-paraphrase. Initially, existing models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and BERT are employed to benchmark performance. Subsequently, more advanced transformer-based models like RoBERTa are proposed to enhance accuracy and semantic understanding. These models leverage pre-trained contextual embeddings and contrastive learning for improved textual similarity detection. The project uses benchmark datasets suitable for sentence-pair classification to train and evaluate the models. While this work is academic in nature, it simulates accurate challenges such as ambiguity, syntax variation, and context preservation. The goal is to understand the strengths and limitations of various models in paraphrase detection and to compare their performance comprehensively. This exploration can contribute to applications such as question-answer matching, duplicate detection, and content summarization systems.

Keywords: Paraphrase Identification, NLP, Sentence Similarity, Classification, Deep Learning,  RoBERTa, BERT, CNN, RNN