Detecting Cross-Language Plagiarism using Open Knowledge Graphs

Corresponding authors: Norman Meuschke, Terry Ruas
Venue: 2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2021)
at the ACM/IEEE Joint Conference on Digital Libraries 2021 (JCDL2021)

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Source code: https://github.com/ag-gipp/cl-osa 

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Dataset Details

ASPEC. The Asian Scientific Paper Excerpt Corpus comprises excepts of scientific papers in Japanese that have been manually translated to English and Chinese. We use both subsets of the ASPEC corpus. 

• ASPEC-JC contains abstracts and paragraphs from the main text of research papers that were translated manually from Japanese to Chinese.
• ASPEC-JE contains abstracts of approx. two million research papers that were translated manually from Japanese to English.  

JRC-Acquis. The corpus consists of legislative texts in 22 languages, which the European Union's Joint Research Centre (JRC) selected from the cumulative body of EU laws (the so called Acquis communautaire). We sampled our test cases from the 10,000 document pairs in the English-French subset of the corpus.

Europarl. The corpus contains transcripts of European Parliament proceedings in 21 European languages. We exclusively sampled test cases from the 9,443 document pairs in the English-French subset of the corpus.

PAN-PC-11. The corpus contains instances of simulated monolingual and cross-language plagiarism that were used for evaluating plagiarism detection methods as part of the workshop series Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection (PAN). Most of the 26,939 documents in the corpus were created by extracting text from openly available books. The documents are partially interspersed with instances of simulated plagiarism that were created and obfuscated automatically or by crowdsourced workers. We exclusively sampled test cases from the 2,921 Spanish-English aligned document pairs in the corpus, for which simulated plagiarism instances were either machine-generated or created manually by crowdsourced workers.

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