Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering
J. Edward Hu, Abhinav Singh, Nils Holzenberger, Matt Post, Benjamin Van Durme · 2019
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 meaningpreserving, 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.