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        <title>Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering
%A Hu, J. Edward
%A Singh, Abhinav
%A Holzenberger, Nils
%A Post, Matt
%A Van Durme, Benjamin
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F hu-etal-2019-large
%X 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.
%R 10.18653/v1/K19-1005
%U https://aclanthology.org/K19-1005
%U https://doi.org/10.18653/v1/K19-1005
%P 44-54

Markdown (Informal)

[Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering](https://aclanthology.org/K19-1005) (Hu et al., CoNLL 2019)

• Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering (Hu et al., CoNLL 2019)

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.