{"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/robust-guidance-for-unsupervised-data","title":"Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation","arxiv_id":"2402.19267","date":"2024-02-29","proceeding":null,"authors":["Seunghyun Ji","Hagai Raja Sinulingga","Darongsae Kwon"],"abstract":"Low-resourced data presents a significant challenge for neural machine translation. In most cases, the low-resourced environment is caused by high costs due to the need for domain experts or the lack of language experts. Therefore, identifying the most training-efficient data within an unsupervised setting emerges as a practical strategy. Recent research suggests that such effective data can be identified by selecting 'appropriately complex data' based on its volume, providing strong intuition for unsupervised data selection. However, we have discovered that establishing criteria for unsupervised data selection remains a challenge, as the 'appropriate level of difficulty' may vary depending on the data domain. We introduce a novel unsupervised data selection method named 'Capturing Perplexing Named Entities,' which leverages the maximum inference entropy in translated named entities as a metric for selection. When tested with the 'Korean-English Parallel Corpus of Specialized Domains,' our method served as robust guidance for identifying training-efficient data across different domains, in contrast to existing methods.","url_abs":"https://arxiv.org/abs/2402.19267v2","url_pdf":"https://arxiv.org/pdf/2402.19267v2.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":"robust-guidance-for-unsupervised-data","repo_url":"https://github.com/comchobo/capturing-perplexing-named-entities","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-translation","task_name":"Machine Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2402.19267","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.19267"}},"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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