{"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/dynamic-data-selection-for-neural-machine","title":"Dynamic Data Selection for Neural Machine Translation","arxiv_id":"1708.00712","date":"2017-08-02","proceeding":"EMNLP 2017 9","authors":["Marlies van der Wees","Arianna Bisazza","Christof Monz"],"abstract":"Intelligent selection of training data has proven a successful technique to\nsimultaneously increase training efficiency and translation performance for\nphrase-based machine translation (PBMT). With the recent increase in popularity\nof neural machine translation (NMT), we explore in this paper to what extent\nand how NMT can also benefit from data selection. While state-of-the-art data\nselection (Axelrod et al., 2011) consistently performs well for PBMT, we show\nthat gains are substantially lower for NMT. Next, we introduce dynamic data\nselection for NMT, a method in which we vary the selected subset of training\ndata between different training epochs. Our experiments show that the best\nresults are achieved when applying a technique we call gradual fine-tuning,\nwith improvements up to +2.6 BLEU over the original data selection approach and\nup to +3.1 BLEU over a general baseline.","url_abs":"http://arxiv.org/abs/1708.00712v1","url_pdf":"http://arxiv.org/pdf/1708.00712v1.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":[{"paper_slug":"dynamic-data-selection-for-neural-machine","repo_url":"https://github.com/marliesvanderwees/dds-nmt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"nmt","task_name":"NMT"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1708.00712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.00712"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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