# ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair

arXiv: https://arxiv.org/abs/2205.04651 · HTML: https://mlsanity.com/paper/2205.04651/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Marian: Fast Neural Machine Translation in C++](https://mlsanity.com/paper/1804.00344/) (2018)
- [Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing](https://mlsanity.com/paper/2004.14564/) (2020)
- [Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing](https://mlsanity.com/paper/2104.10097/) (2021)
- [Learning to Paraphrase for Question Answering](https://mlsanity.com/paper/1708.06022/) (2017)
- [ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations](https://mlsanity.com/paper/1711.05732/) (2017)
- [Paraphrase Augmented Task-Oriented Dialog Generation](https://mlsanity.com/paper/2004.07462/) (2020)
- [ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation](https://mlsanity.com/paper/1901.03644/) (2019)

## Built on by (most cited first, 6 of 6)

- [T5Score: Discriminative Fine-tuning of Generative Evaluation Metrics](https://mlsanity.com/paper/2212.05726/) (2022)
- [Toward Informal Language Processing: Knowledge of Slang in Large Language Models](https://mlsanity.com/paper/2404.02323/) (2024)
- [A survey of diversity quantification in natural language processing: The why, what, where and how](https://mlsanity.com/paper/2507.20858/) (2025)
- [NusaCrowd: A Call for Open and Reproducible NLP Research in Indonesian Languages](https://mlsanity.com/paper/2207.10524/) (2022)
- [MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models](https://mlsanity.com/paper/2508.17444/) (2025)
- [Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models](https://mlsanity.com/paper/2407.16565/) (2024)

## Abstract (arXiv)

We release our synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Romanian, Russian, Swedish, Vietnamese, and Chinese. Our method relies only on monolingual data and a neural machine translation system to generate paraphrases, hence simple to apply. We generate multiple translation samples using beam search and choose the most lexically diverse pair according to their sentence BLEU. We compare our generated corpus with the textttParaBank2. According to our evaluation, our synthetic paraphrase pairs are semantically similar and lexically diverse.