Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation

Ashutosh Kumar, Satwik Bhattamishra, Manik Bhandari, Partha Talukdar


Abstract
Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents. Previous paraphrasing approaches have mainly focused on the issue of generating semantically similar paraphrases while paying little attention towards diversity. In fact, most of the methods rely solely on top-k beam search sequences to obtain a set of paraphrases. The resulting set, however, contains many structurally similar sentences. In this work, we focus on the task of obtaining highly diverse paraphrases while not compromising on paraphrasing quality. We provide a novel formulation of the problem in terms of monotone submodular function maximization, specifically targeted towards the task of paraphrasing. Additionally, we demonstrate the effectiveness of our method for data augmentation on multiple tasks such as intent classification and paraphrase recognition. In order to drive further research, we have made the source code available.
Anthology ID:
N19-1363
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3609–3619
Language:
URL:
https://aclanthology.org/N19-1363
DOI:
10.18653/v1/N19-1363
Bibkey:
Cite (ACL):
Ashutosh Kumar, Satwik Bhattamishra, Manik Bhandari, and Partha Talukdar. 2019. Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3609–3619, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation (Kumar et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1363.pdf
Supplementary:
 N19-1363.Supplementary.pdf
Presentation:
 N19-1363.Presentation.pdf
Video:
 https://vimeo.com/356143764
Code
 malllabiisc/DiPS