Paraphrase detection is important for a number of applications, including plagiarism detection, authorship attribution, question answering, text summarization, text mining in general, etc. In this paper, we give a performance overview of various types of corpus-based models, especially deep learning (DL) models, with the task of paraphrase detection. We report the results of eight models (LSI, TF-IDF, Word2Vec, Doc2Vec, GloVe, FastText, ELMO, and USE) evaluated on three different public available corpora: Microsoft Research Paraphrase Corpus, Clough and Stevenson and Webis Crowd Paraphrase Corpus 2011. Through a great number of experiments, we decided on the most appropriate approaches for text pre-processing: hyper-parameters, sub-model selection—where they exist (e.g., Skipgram vs. CBOW), distance measures, and semantic similarity/paraphrase detection threshold. Our findings and those of other researchers who have used deep learning models show that DL models are very competitive with traditional state-of-the-art approaches and have potential that should be further developed.

https://www.mdpi.com/2078-2489/11/5/241/pdf

Corpus-Based Paraphrase Detection Experiments and Review

Paraphrase detection is important for a number of applications, including plagiarism detection, authorship attribution, question answering, text summarization, text mining in general, etc. In this paper, we give a performance overview of various types of corpus-based models, especially deep learning (DL) models, with the task of paraphrase detection. We report the results of eight models (LSI, TF-IDF, Word2Vec, Doc2Vec, GloVe, FastText, ELMO, and USE) evaluated on three different public available corpora: Microsoft Research Paraphrase Corpus, Clough and Stevenson and Webis Crowd Paraphrase Corpus 2011. Through a great number of experiments, we decided on the most appropriate approaches for text pre-processing: hyper-parameters, sub-model selection-where they exist (e.g., Skipgram vs. CBOW), distance measures, and semantic similarity/paraphrase detection threshold. Our findings and those of other researchers who have used deep learning models show that DL models are very competitive with traditional state-of-the-art approaches and have potential that should be further developed.

Paraphrase detection (PD) is a very essential and important task in Natural language processing. The goal of paraphrase detection is to check whether two statements written in natural language have the identical semantic or not. Its importance appears in many fields like plagiarism detection, question answering, document clustering and information retrieval, etc. This paper proposes a hybrid model that combines the text similarity approach with deep learning approach in order to improve paraphrase detection. This model verified results with Microsoft Research Paraphrase Corpus (MSPR) dataset, shows that accuracy measure is about 76.6% and F-measure is about 83.5%.

/pdf/a-hybrid-model-for-paraphrase-detection-combines-pros-of-4bpzdulc1g.pdf

A Hybrid Model for Paraphrase Detection Combines pros of Text Similarity with Deep Learning

Hungarian layer: A novel interpretable neural layer for paraphrase identification

The rapid advancement of AI technology has made text generation tools like GPT-3 and ChatGPT increasingly accessible, scalable, and effective. This can pose serious threat to the credibility of various forms of media if these technologies are used for plagiarism, including scientific literature and news sources. Despite the development of automated methods for paraphrase identification, detecting this type of plagiarism remains a challenge due to the disparate nature of the datasets on which these methods are trained. In this study, we review traditional and current approaches to paraphrase identification and propose a refined typology of paraphrases. We also investigate how this typology is represented in popular datasets and how under-representation of certain types of paraphrases impacts detection capabilities. Finally, we outline new directions for future research and datasets in the pursuit of more effective paraphrase detection using AI.

Paraphrase Identification with Deep Learning: A Review of Datasets and Methods

This study is to examine paraphrase detection (PD) for diagnostic purposes. Which is defined as the capability to find and discover the similarity between sentences that are written in a natural language? Where detecting similar sentences written in natural language is extreme importance and it is very essential for computer software used in plagiarism detection, Q and A automated systems, text mining, authorship authentication and text recapitulation. The goal of paraphrase detection is to detect whether two statements have the identical semantic or not. There is hundreds of empirical research in this direction. This study will focus on the discussion of recent studies of the PD methods and will categorize them in two categories, supervised learning and unsupervised learning. Also will give an idea about text similarity, machine learning and deep learning approaches. The performance of the selected researches is assessed by how accurate the F-measures are in detecting paraphrase in Microsoft Research Paraphrase Corpus (MSPR).

/pdf/exploring-the-recent-trends-of-paraphrase-detection-291uupdc7h.pdf

I Mohamed

Papers

Exploring the Recent Trends of Paraphrase Detection