ParaBank v1.0 Full (~9 GB) (zip), ParaBank v1.0 Large, 50m pairs (~3 GB) (zip), ParaBank v1.0 Small Diverse, 5m pairs (zip), ParaBank v1.0 Large Diverse, 50m pairs (zip)


We present ParaBank, a large-scale English paraphrase dataset that surpasses prior work in both quantity and quality. Following the approach of ParaNMT, we train a Czech-English neural machine translation (NMT) system to generate novel paraphrases of English reference sentences. By adding lexical constraints to the NMT decoding procedure, however, we are able to produce multiple high-quality sentential paraphrases per source sentence, yielding an English paraphrase resource with more than 4 billion generated tokens and exhibiting greater lexical diversity. Using human judgments, we also demonstrate that ParaBank’s paraphrases improve over ParaNMT on both semantic similarity and fluency. Finally, we use ParaBank to train a monolingual NMT model with the same support for lexically-constrained decoding for sentence rewriting tasks.

For a detailed description of the collection and our methods, please see the following papers:

Hu, J. E., R. Rudinger, M. Post, & B. Van Durme. 2019. ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation. Proceedings of AAAI 2019, Honolulu, Hawaii, January 26 – Feb 1, 2019.

To interact with the monolingual rewriter described in the paper, please check out this live demo. The rewriter can be downloaded here. Please download the model parameters here and place under the rewriter directory.

Our evaluation data is available for download here.


Benjamin Van Durme bio photo
Benjamin Van Durme
Matt Post bio photo
Matt Post
Rachel Rudinger bio photo
Rachel Rudinger
Edward Hu bio photo
Edward Hu