# A Deep Network Model for Paraphrase Detection in Short Text Messages

arXiv: https://arxiv.org/abs/1712.02820 · HTML: https://mlsanity.com/paper/1712.02820/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2017.

## Built on by (most cited first, 9 of 9)

- [Do Language Models Plagiarize?](https://mlsanity.com/paper/2203.07618/) (2022)
- [Paraphrase Identification with Deep Learning: A Review of Datasets and Methods](https://mlsanity.com/paper/2212.06933/) (2022)
- [Corpus-Based Paraphrase Detection Experiments and Review](https://mlsanity.com/paper/2106.00145/) (2021)
- [FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models](https://mlsanity.com/paper/2406.00839/) (2024)
- [Enhancing Plagiarism Detection in Marathi with a Weighted Ensemble of TF-IDF and BERT Embeddings for Low-Resource Language Processing](https://mlsanity.com/paper/2501.05260/) (2025)
- [Recognizing semantic relation in sentence pairs using Tree-RNNs and Typed dependencies](https://mlsanity.com/paper/2201.04810/) (2022)
- [MuLVE, A Multi-Language Vocabulary Evaluation Data Set](https://mlsanity.com/paper/2201.06286/) (2022)
- [The power of text similarity in identifying AI-LLM paraphrased documents: The case of BBC news articles and ChatGPT](https://mlsanity.com/paper/2505.12405/) (2025)
- [Assessing Reliability of BERT-Based Models on Question Answering Tasks](https://mlsanity.com/paper/2608.10806/) (2026)

## Abstract (arXiv)

This paper is concerned with paraphrase detection. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Given two sentences, the objective is to detect whether they are semantically identical. An important insight from this work is that existing paraphrase systems perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts. Challenges with paraphrase detection on user generated short texts, such as Twitter, include language irregularity and noise. To cope with these challenges, we propose a novel deep neural network-based approach that relies on coarse-grained sentence modeling using a convolutional neural network and a long short-term memory model, combined with a specific fine-grained word-level similarity matching model. Our experimental results show that the proposed approach outperforms existing state-of-the-art approaches on user-generated noisy social media data, such as Twitter texts, and achieves highly competitive performance on a cleaner corpus.