{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/parabank-monolingual-bitext-generation-and","title":"ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation","arxiv_id":"1901.03644","date":"2019-01-11","proceeding":null,"authors":["J. Edward Hu","Rachel Rudinger","Matt Post","Benjamin Van Durme"],"abstract":"We present ParaBank, a large-scale English paraphrase dataset that surpasses\nprior work in both quantity and quality. Following the approach of ParaNMT, we\ntrain a Czech-English neural machine translation (NMT) system to generate novel\nparaphrases of English reference sentences. By adding lexical constraints to\nthe NMT decoding procedure, however, we are able to produce multiple\nhigh-quality sentential paraphrases per source sentence, yielding an English\nparaphrase resource with more than 4 billion generated tokens and exhibiting\ngreater lexical diversity. Using human judgments, we also demonstrate that\nParaBank's paraphrases improve over ParaNMT on both semantic similarity and\nfluency. Finally, we use ParaBank to train a monolingual NMT model with the\nsame support for lexically-constrained decoding for sentence rewriting tasks.","url_abs":"http://arxiv.org/abs/1901.03644v1","url_pdf":"http://arxiv.org/pdf/1901.03644v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"nmt","task_name":"NMT"},{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-rewriting","task_name":"Sentence ReWriting"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[{"slug":"parabank","name":"ParaBank","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.03644","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}