Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cross-Language Ontology-Based Similarity Analysis (CL\nobreakdash-OSA) for this task. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. Opposed to other methods, CL\nobreakdash-OSA does not require computationally expensive machine translation, nor pre-training using comparable or parallel corpora. It reliably disambiguates homonyms and scales to allow its application to Web-scale document collections. We show that CL-OSA outperforms state-of-the-art methods for retrieving candidate documents from five large, topically diverse test corpora that include distant language pairs like Japanese-English. For identifying cross-language plagiarism at the character level, CL-OSA primarily improves the detection of sense-for-sense translations. For these challenging cases, CL-OSA's performance in terms of the well-established PlagDet score exceeds that of the best competitor by more than factor two. The code and data of our study are openly available.
翻译:辨别跨语言的图象是挑战性的, 特别是对于远程语言配对和感知感感感感知译文来说, 辨别跨语言的图象是挑战性的。 我们为此任务采用了新的多语种检索模型( CL\ nobreakdash- OSA ) 。 CL- OSA 代表了从开放知识图维基数据中获得的实体矢量文件 。 CL\ nobreakdash- OSA 与其他方法相比, CL\ nobredash- OSA 并不需要计算昂贵的机器翻译, 也不需要使用可比或平行公司进行预培训。 它可靠地排除同音和比例表, 以便将其应用到网络规模的文件收藏中。 我们显示, CL- OSA 超越了从五个大型、 主题多样化的测试体系中检索候选文件的最先进的方法, 包括像日文那样的远语言配体。 为了在字符级别上识别跨语言的成像, CL- OSA 主要是改进感知感知感知的翻译的检测。 对于这些具有挑战性的案例, CL- OSA 的成绩比我们现有两个已建立好的数据分数分数的成绩要好。