{"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/capturing-longer-context-for-document-level","title":"Rethinking Document-level Neural Machine Translation","arxiv_id":"2010.08961","date":"2020-10-18","proceeding":"Findings (ACL) 2022 5","authors":["Zewei Sun","Mingxuan Wang","Hao Zhou","Chengqi Zhao","ShuJian Huang","Jiajun Chen","Lei LI"],"abstract":"This paper does not aim at introducing a novel model for document-level neural machine translation. Instead, we head back to the original Transformer model and hope to answer the following question: Is the capacity of current models strong enough for document-level translation? Interestingly, we observe that the original Transformer with appropriate training techniques can achieve strong results for document translation, even with a length of 2000 words. We evaluate this model and several recent approaches on nine document-level datasets and two sentence-level datasets across six languages. 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