PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation

Hongyuan Lu, Wai Lam


Abstract
Curriculum Data Augmentation (CDA) improves neural models by presenting synthetic data with increasing difficulties from easy to hard. However, traditional CDA simply treats the ratio of word perturbation as the difficulty measure and goes through the curriculums only once. This paper presents PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation, a novel CDA framework via paraphrasing, which exploits the textual paraphrase similarity as the curriculum difficulty measure. We propose a curriculum-aware paraphrase generation module composed of three units: a paraphrase candidate generator with bottom-k sampling, a filtering mechanism and a difficulty measure. We also propose a cyclic learning strategy that passes through the curriculums multiple times. The bottom-k sampling is proposed to generate super-hard instances for the later curriculums. Experimental results on few-shot text classification as well as dialogue generation indicate that PCC surpasses competitive baselines. Human evaluation and extensive case studies indicate that bottom-k sampling effectively generates super-hard instances, and PCC significantly improves the baseline dialogue agent.
Anthology ID:
2023.eacl-main.5
Volume:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
68–82
Language:
URL:
https://aclanthology.org/2023.eacl-main.5
DOI:
10.18653/v1/2023.eacl-main.5
Award:
 EACL Outstanding Paper
Bibkey:
Cite (ACL):
Hongyuan Lu and Wai Lam. 2023. PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 68–82, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation (Lu & Lam, EACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.eacl-main.5.pdf
Video:
 https://aclanthology.org/2023.eacl-main.5.mp4