# A Large-Scale Benchmark for Vietnamese Sentence Paraphrases

arXiv: https://arxiv.org/abs/2502.07188 · HTML: https://mlsanity.com/paper/2502.07188/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 52 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [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)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (2018)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks](https://mlsanity.com/paper/2304.10145/) (2023)
- [Learning to Paraphrase for Question Answering](https://mlsanity.com/paper/1708.06022/) (2017)
- [A Challenge Set Approach to Evaluating Machine Translation](https://mlsanity.com/paper/1704.07431/) (2017)
- [ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation](https://mlsanity.com/paper/2205.06457/) (2022)
- [A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese](https://mlsanity.com/paper/2010.01891/) (2020)
- [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://mlsanity.com/paper/2109.09701/) (2021)
- [A Vietnamese Dataset for Evaluating Machine Reading Comprehension](https://mlsanity.com/paper/2009.14725/) (2020)
- [ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation](https://mlsanity.com/paper/2101.08382/) (2021)
- [New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles](https://mlsanity.com/paper/2006.11138/) (2020)
- [Quick Starting Dialog Systems with Paraphrase Generation](https://mlsanity.com/paper/2204.02546/) (2022)

## Built on by (most cited first, 1 of 1)

- [VIVID: A Culturally Grounded Benchmark Exposing the Figurative Language Gap in Vietnamese NLP](https://mlsanity.com/paper/2608.03095/) (2026)

## Abstract (arXiv)

This paper presents ViSP, a high-quality Vietnamese dataset for sentence paraphrasing, consisting of 1.2M original-paraphrase pairs collected from various domains. The dataset was constructed using a hybrid approach that combines automatic paraphrase generation with manual evaluation to ensure high quality. We conducted experiments using methods such as back-translation, EDA, and baseline models like BART and T5, as well as large language models (LLMs), including GPT-4o, Gemini-1.5, Aya, Qwen-2.5, and Meta-Llama-3.1 variants. To the best of our knowledge, this is the first large-scale study on Vietnamese paraphrasing. We hope that our dataset and findings will serve as a valuable foundation for future research and applications in Vietnamese paraphrase tasks.