Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering
J. Edward Hu, Abhinav Singh, Nils Holzenberger, Matt Post, Benjamin Van Durme
Abstract
Producing diverse paraphrases of a sentence is a challenging task. Natural paraphrase corpora are scarce and limited, while existing large-scale resources are automatically generated via back-translation and rely on beam search, which tends to lack diversity. We describe ParaBank 2, a new resource that contains multiple diverse sentential paraphrases, produced from a bilingual corpus using negative constraints, inference sampling, and clustering.We show that ParaBank 2 significantly surpasses prior work in both lexical and syntactic diversity while being meaning-preserving, as measured by human judgments and standardized metrics. Further, we illustrate how such paraphrastic resources may be used to refine contextualized encoders, leading to improvements in downstream tasks.- Anthology ID:
- K19-1005
- Volume:
- Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 44–54
- Language:
- URL:
- https://aclanthology.org/K19-1005
- DOI:
- 10.18653/v1/K19-1005
- Cite (ACL):
- J. Edward Hu, Abhinav Singh, Nils Holzenberger, Matt Post, and Benjamin Van Durme. 2019. Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 44–54, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering (Hu et al., CoNLL 2019)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/K19-1005.pdf