Plagiarism and text reuse become more available with the Internet development. Therefore it is important to check scientific papers for the fact of cheating, especially in Academia. Existing systems of plagiarism detection show the good performance and have a huge source databases. Thus now it is not enough just to copy the text as is from the source document to get the original work. Therefore, another type of plagiarism become popular -- cross-lingual plagiarism. We present a CrossLang system for such kind of plagiarism detection for English-Russian language pair. We introduced CrossLang -a framework for cross-lingual plagiarism detection for English Russian language pair. We decomposed the problem of cross-lingual plagiarism detection into several stages and provide a service, consists of a set of microservices. The CrossLang use a monolingual approachreducing the problem to the one language. For this purpose we trained the neural machine translation system. Another two main algoithmic components are Source Retrieval and Document Comparison stages. For the Source Retrieval problem we used a modification of shingling method that allow us to deal with ambiguity after translation. For the Document Comparison stage we used phrase embeddings that were trained with slight supervision. We evaluated the effectiveness of main stages.
A system for cross-lingual (English-Russian) plagiarism detection
{"src": "scitldr-AIC_train", "paper_id": "BkxiG6qqIr"}