TY - GEN

T1 - Automatically Ranked Russian Paraphrase Corpus for Text Generation

AU - Gudkov, Vadim

AU - Mitrofanova, Olga

AU - Filippskikh, Elizaveta

N1 - Vadim Gudkov, Olga Mitrofanova, and Elizaveta Filippskikh. 2020. Automatically Ranked Russian Paraphrase Corpus for Text Generation. In Proceedings of the Fourth Workshop on Neural Generation and Translation, pages 54–59, Online. Association for Computational Linguistics.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

UR - https://arxiv.org/abs/2006.09719

M3 - Conference contribution

SP - 54

EP - 59

BT - Proceedings of the Fourth Workshop on Neural Generation and Translation

CY - ACL

Y2 - 5 July 2020 through 10 July 2020

ER -