Deep Investigation of Cross-Language Plagiarism Detection Methods
arXiv:1705.08828
Abstract
This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts). We investigate cross-language plagiarism detection methods for 6 language pairs on 2 granularities of text units in order to draw robust conclusions on the best methods while deeply analyzing correlations across document styles and languages.
Accepted to BUCC (10th Workshop on Building and Using Comparable Corpora) colocated with ACL 2017