Paraphrase Generation: A Review from RNN to Transformer based Approaches-Reference-Cited by-同舟云学术

Paraphrase Generation: A Review from RNN to Transformer based Approaches

Published:2022-04-01 Issue: Volume: Page:
ISSN:0976-5034
Container-title:International Journal of Next-Generation Computing
language:
Short-container-title:ijngc

Author:

Singh Arwinder,Gurpreet Singh Josan

Abstract

Paraphrasing is an act of generating similar text to the source text with different expressions. Paraphrase generation is an important task in various Natural Language Processing applications such as machine translation, question-answering, information re

Publisher

Perpetual Innovation Media Pvt. Ltd.

Reference101 articles.

1. Abujabal, A., Saha Roy, R., Yahya, M., and Weikum, G. 2019. ComQA: A community- sourced dataset for complex factoid question answering with paraphrase clusters. In Pro- ceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Minneapolis, Minnesota, 307–317.

2. Agarwal, B., Ramampiaro, H., Langseth, H., and Ruocco, M. 2018. A deep network model for paraphrase detection in short text messages. Infmation Processing and Manage- ment 54, 922–937.

3. Aggarwal, M., Kumari, N., Bansal, A., and Krishnamurthy, B. 2018. Redecode frame- work for iterative improvement in paraphrase generation. CoRR abs/1811.04454, 1–8.

4. Bahdanau, D., Cho, K., and Bengio, Y. 2014. Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473, 1409.0473.

5. Bannard, C. and Callison-Burch, C. 2005. Paraphrasing with bilingual parallel corpora. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05). Association for Computational Linguistics, Ann Arbor, Michigan, 597–604.