{"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/a-benchmark-dataset-for-multi-level","title":"A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation","arxiv_id":null,"date":"2022-06-01","proceeding":"LREC 2022 6","authors":["Kazuki Tani","Ryoya Yuasa","Kazuki Takikawa","Akihiro Tamura","Tomoyuki Kajiwara","Takashi Ninomiya","Tsuneo Kato"],"abstract":"This paper presents a new benchmark test dataset for multi-level complexity-controllable machine translation (MLCC-MT), which is MT controlling the complexity of the output at more than two levels. In previous research, MLCC-MT models have been evaluated on a test dataset automatically constructed from the Newsela corpus, which is a document-level comparable corpus with document-level complexity. The existing test dataset has the following three problems: (i) A source language sentence and its target language sentence are not necessarily an exact translation pair because they are automatically detected. (ii) A target language sentence and its simplified target language sentence are not necessarily exactly parallel because they are automatically aligned. (iii) A sentence-level complexity is not necessarily appropriate because it is transferred from an article-level complexity attached to the Newsela corpus. Therefore, we create a benchmark test dataset for Japanese-to-English MLCC-MT from the Newsela corpus by introducing an automatic filtering of data with inappropriate sentence-level complexity, manual check for parallel target language sentences with different complexity levels, and manual translation. Moreover, we implement two MLCC-NMT frameworks with a Transformer architecture and report their performance on our test dataset as baselines for future research. Our test dataset and codes are released.","url_abs":"https://aclanthology.org/2022.lrec-1.726","url_pdf":"https://aclanthology.org/2022.lrec-1.726.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":"a-benchmark-dataset-for-multi-level","repo_url":"https://github.com/k-t4n1/a-benchmarkdataset-for-complexitycontrollablenmt","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":"sentence","task_name":"Sentence"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}