# Paraphrase Types for Generation and Detection

arXiv: https://arxiv.org/abs/2310.14863 · HTML: https://mlsanity.com/paper/2310.14863/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 55 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://mlsanity.com/paper/1907.12412/) (2019)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling](https://mlsanity.com/paper/1811.10996/) (2018)
- [A Continuously Growing Dataset of Sentential Paraphrases](https://mlsanity.com/paper/1708.00391/) (2017)
- [Neural Syntactic Preordering for Controlled Paraphrase Generation](https://mlsanity.com/paper/2005.02013/) (2020)
- [Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs](https://mlsanity.com/paper/2101.10579/) (2021)
- [Controllable Paraphrase Generation with a Syntactic Exemplar](https://mlsanity.com/paper/1906.00565/) (2019)
- [How Large Language Models are Transforming Machine-Paraphrased Plagiarism](https://mlsanity.com/paper/2210.03568/) (2022)
- [Quality Controlled Paraphrase Generation](https://mlsanity.com/paper/2203.10940/) (2022)
- [Decomposable Neural Paraphrase Generation](https://mlsanity.com/paper/1906.09741/) (2019)
- [Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection](https://mlsanity.com/paper/2103.12450/) (2021)
- [ConRPG: Paraphrase Generation using Contexts as Regularizer](https://mlsanity.com/paper/2109.00363/) (2021)
- [Specialized Document Embeddings for Aspect-based Similarity of Research Papers](https://mlsanity.com/paper/2203.14541/) (2022)
- [Paraphrase Detection: Human vs. Machine Content](https://mlsanity.com/paper/2303.13989/) (2023)
- [Improving Large-scale Paraphrase Acquisition and Generation](https://mlsanity.com/paper/2210.03235/) (2022)
- [Principled Paraphrase Generation with Parallel Corpora](https://mlsanity.com/paper/2205.12213/) (2022)
- [PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation](https://mlsanity.com/paper/2305.16701/) (2023)
- [Paraphrases do not explain word analogies](https://mlsanity.com/paper/2102.11749/) (2021)

## Built on by (most cited first, 4 of 4)

- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [Amuro and Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2408.06663/) (2024)
- [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)
- [Diverse Word Choices, Same Reference: Annotating Lexically-Rich Cross-Document Coreference](https://mlsanity.com/paper/2602.17424/) (2026)

## Abstract (arXiv)

Current approaches in paraphrase generation and detection heavily rely on a single general similarity score, ignoring the intricate linguistic properties of language. This paper introduces two new tasks to address this shortcoming by considering paraphrase types - specific linguistic perturbations at particular text positions. We name these tasks Paraphrase Type Generation and Paraphrase Type Detection. Our results suggest that while current techniques perform well in a binary classification scenario, i.e., paraphrased or not, the inclusion of fine-grained paraphrase types poses a significant challenge. While most approaches are good at generating and detecting general semantic similar content, they fail to understand the intrinsic linguistic variables they manipulate. Models trained in generating and identifying paraphrase types also show improvements in tasks without them. In addition, scaling these models further improves their ability to understand paraphrase types. We believe paraphrase types can unlock a new paradigm for developing paraphrase models and solving tasks in the future.