{"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/upping-the-ante-towards-a-better-benchmark","title":"Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation","arxiv_id":"1805.01676","date":"2018-05-04","proceeding":"LREC 2018 5","authors":["Christian Hadiwinoto","Hwee Tou Ng"],"abstract":"There are many machine translation (MT) papers that propose novel approaches\nand show improvements over their self-defined baselines. The experimental\nsetting in each paper often differs from one another. As such, it is hard to\ndetermine if a proposed approach is really useful and advances the state of the\nart. Chinese-to-English translation is a common translation direction in MT\npapers, although there is not one widely accepted experimental setting in\nChinese-to-English MT. Our goal in this paper is to propose a benchmark in\nevaluation setup for Chinese-to-English machine translation, such that the\neffectiveness of a new proposed MT approach can be directly compared to\nprevious approaches. Towards this end, we also built a highly competitive\nstate-of-the-art MT system trained on a large-scale training set. Our system\noutperforms reported results on NIST OpenMT test sets in almost all papers\npublished in major conferences and journals in computational linguistics and\nartificial intelligence in the past 11 years. We argue that a standardized\nbenchmark on data and performance is important for meaningful comparison.","url_abs":"http://arxiv.org/abs/1805.01676v1","url_pdf":"http://arxiv.org/pdf/1805.01676v1.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":"upping-the-ante-towards-a-better-benchmark","repo_url":"https://github.com/nusnlp/c2e-mt-benchmark","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":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}