The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
Open accessAbstract
We describe PARANMT-50M, a dataset of more than 50 million English-English sentential paraphrase pairs.We generated the pairs automatically by using neural machine translation to translate the non-English side of a large parallel corpus, following Wieting et al. (2017).Our hope is that PARANMT-50M can be a valuable resource for paraphrase generation and can provide a rich source of semantic knowledge to improve downstream natural language understanding tasks.To show its utility, we use PARANMT-50M to train paraphrastic sentence embeddings that outperform all supervised systems on every SemEval semantic textual similarity competition, in addition to showing how it can be used for paraphrase generation. 1
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Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
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Self-citationA Survey of Evaluation Metrics Used for NLG Systems
Sai, A. B., Mohankumar, A. K., & Khapra, M. M. (2023) · ACM Computing Surveys · doi:10.1145/3485766 · Cited by 235
Editing Factual Knowledge in Language Models
De Cao, N., Aziz, W., & Titov, I. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.522 · Cited by 180
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
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Metadata
- Authors
- Wieting, J., & Gimpel, K.
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 451-462
- OpenAlex
- W2951718443
- Cited by
- 317
- References
- 57
- FWCI
- 20.29
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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