mlsanityData to 7 Oct 2026

Corpus-Based Paraphrase Detection Experiments and Review

arXiv 2106.00145 ↗First posted 31 May 2021cs.CL cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 74 references that are in the map. First posted on arXiv 31 May 2021.

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19 of 74 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  3. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  4. 2018Deep contextualized word representationscited by 2,285
  5. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  6. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  7. 2016Bag of Tricks for Efficient Text Classificationcited by 869
  8. 2014Distributed Representations of Sentences and Documentscited by 677
  9. 2018Universal Sentence Encodercited by 485
  10. 2019What do you learn from context? Probing for sentence structure in contextualized word representationscited by 436
  11. 2017A Survey Of Cross-lingual Word Embedding Modelscited by 142
  12. 2017Paraphrase Generation with Deep Reinforcement Learningcited by 58
7 more
  1. 2020Testing of Support Tools for Plagiarism Detectioncited by 11
  2. 2017A Deep Network Model for Paraphrase Detection in Short Text Messagescited by 9
  3. 2014Evaluating Neural Word Representations in Tensor-Based Compositional Settingscited by 7
  4. 2017Learning Multi-Modal Word Representation Grounded in Visual Contextcited by 7
  5. 2019Improving Tree-LSTM with Tree Attentioncited by 6
  6. 2015Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaningcited by 3
  7. 2017Methods for Detecting Paraphrase Plagiarismcited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 1.

5 papers.

  1. 2022Paraphrase Identification with Deep Learning: A Review of Datasets and Methodscited by 13
  2. 2023A Bayesian approach for prompt optimization in pre-trained language modelscited by 4
  3. 2021Semantic Search as Extractive Paraphrase Span Detectioncited by 2
  4. 2024Discourse Features Enhance Detection of Document-Level Machine-Generated Contentcited by 1
  5. 2025The power of text similarity in identifying AI-LLM paraphrased documents: The case of BBC news articles and ChatGPTcited by 0