Quality Controlled Paraphrase Generation

Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer, Ilya Shnayderman, Noam Slonim, Liat Ein-Dor


Abstract
Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, the original sentence. Generating high-quality paraphrases is challenging as it becomes increasingly hard to preserve meaning as linguistic diversity increases. Recent works achieve nice results by controlling specific aspects of the paraphrase, such as its syntactic tree. However, they do not allow to directly control the quality of the generated paraphrase, and suffer from low flexibility and scalability. Here we propose QCPG, a quality-guided controlled paraphrase generation model, that allows directly controlling the quality dimensions. Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases. We show that our method is able to generate paraphrases which maintain the original meaning while achieving higher diversity than the uncontrolled baseline. The models, the code, and the data can be found in https://github.com/IBM/quality-controlled-paraphrase-generation.
Anthology ID:
2022.acl-long.45
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
596–609
Language:
URL:
https://aclanthology.org/2022.acl-long.45
DOI:
10.18653/v1/2022.acl-long.45
Bibkey:
Cite (ACL):
Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer, Ilya Shnayderman, Noam Slonim, and Liat Ein-Dor. 2022. Quality Controlled Paraphrase Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 596–609, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Quality Controlled Paraphrase Generation (Bandel et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.45.pdf
Code
 ibm/quality-controlled-paraphrase-generation
Data
COCO