{"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/bilingual-expert-can-find-translation-errors","title":"\"Bilingual Expert\" Can Find Translation Errors","arxiv_id":"1807.09433","date":"2018-07-25","proceeding":null,"authors":["Kai Fan","Jiayi Wang","Bo Li","Fengming Zhou","Boxing Chen","Luo Si"],"abstract":"Recent advances in statistical machine translation via the adoption of neural\nsequence-to-sequence models empower the end-to-end system to achieve\nstate-of-the-art in many WMT benchmarks. The performance of such machine\ntranslation (MT) system is usually evaluated by automatic metric BLEU when the\ngolden references are provided for validation. However, for model inference or\nproduction deployment, the golden references are prohibitively available or\nrequire expensive human annotation with bilingual expertise. In order to\naddress the issue of quality evaluation (QE) without reference, we propose a\ngeneral framework for automatic evaluation of translation output for most WMT\nquality evaluation tasks. We first build a conditional target language model\nwith a novel bidirectional transformer, named neural bilingual expert model,\nwhich is pre-trained on large parallel corpora for feature extraction. For QE\ninference, the bilingual expert model can simultaneously produce the joint\nlatent representation between the source and the translation, and real-valued\nmeasurements of possible erroneous tokens based on the prior knowledge learned\nfrom parallel data. Subsequently, the features will further be fed into a\nsimple Bi-LSTM predictive model for quality evaluation. The experimental\nresults show that our approach achieves the state-of-the-art performance in the\nquality estimation track of WMT 2017/2018.","url_abs":"http://arxiv.org/abs/1807.09433v3","url_pdf":"http://arxiv.org/pdf/1807.09433v3.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":"bilingual-expert-can-find-translation-errors","repo_url":"https://github.com/lovecambi/qebrain","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.09433","atlas_url":"https://app.syntology.ai/?focus=1807.09433","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}