{"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/towards-recognizing-phrase-translation","title":"Towards Recognizing Phrase Translation Processes: Experiments on English-French","arxiv_id":"1904.12213","date":"2019-04-27","proceeding":null,"authors":["Yuming Zhai","Pooyan Safari","Gabriel Illouz","Alexandre Allauzen","Anne Vilnat"],"abstract":"When translating phrases (words or group of words), human translators,\nconsciously or not, resort to different translation processes apart from the\nliteral translation, such as Idiom Equivalence, Generalization,\nParticularization, Semantic Modulation, etc. Translators and linguists (such as\nVinay and Darbelnet, Newmark, etc.) have proposed several typologies to\ncharacterize the different translation processes. However, to the best of our\nknowledge, there has not been effort to automatically classify these\nfine-grained translation processes. Recently, an English-French parallel corpus\nof TED Talks has been manually annotated with translation process categories,\nalong with established annotation guidelines. Based on these annotated\nexamples, we propose an automatic classification of translation processes at\nsubsentential level. Experimental results show that we can distinguish\nnon-literal translation from literal translation with an accuracy of 87.09%,\nand 55.20% for classifying among five non-literal translation processes. This\nwork demonstrates that it is possible to automatically classify translation\nprocesses. Even with a small amount of annotated examples, our experiments show\nthe directions that we can follow in future work. One of our long term\nobjectives is leveraging this automatic classification to better control\nparaphrase extraction from bilingual parallel corpora.","url_abs":"http://arxiv.org/abs/1904.12213v1","url_pdf":"http://arxiv.org/pdf/1904.12213v1.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":"towards-recognizing-phrase-translation","repo_url":"https://github.com/YumingZHAI/ctp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.12213","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}