# Automatically Ranked Russian Paraphrase Corpus for Text Generation

arXiv: https://arxiv.org/abs/2006.09719 · HTML: https://mlsanity.com/paper/2006.09719/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design](https://mlsanity.com/paper/2408.12503/) (2024)
- [Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxification](https://mlsanity.com/paper/2311.13937/) (2023)

## Abstract (arXiv)

The article is focused on automatic development and ranking of a large corpus for Russian paraphrase generation which proves to be the first corpus of such type in Russian computational linguistics. Existing manually annotated paraphrase datasets for Russian are limited to small-sized ParaPhraser corpus and ParaPlag which are suitable for a set of NLP tasks, such as paraphrase and plagiarism detection, sentence similarity and relatedness estimation, etc. Due to size restrictions, these datasets can hardly be applied in end-to-end text generation solutions. Meanwhile, paraphrase generation requires a large amount of training data. In our study we propose a solution to the problem: we collect, rank and evaluate a new publicly available headline paraphrase corpus (ParaPhraser Plus), and then perform text generation experiments with manual evaluation on automatically ranked corpora using the Universal Transformer architecture.