Detecting Cross-Language Plagiarism using Open Knowledge Graphs

TL;DR

This paper introduces CL-OSA, a multilingual retrieval model that detects cross-language plagiarism by representing documents as entity vectors from Wikidata, avoiding machine translation and pre-training, and outperforming existing methods especially for distant language pairs.

Contribution

The paper presents CL-OSA, a novel knowledge graph-based model for cross-language plagiarism detection that is scalable, effective for distant languages, and does not rely on translation or pre-training.

Findings

CL-OSA outperforms state-of-the-art methods on diverse corpora.

It effectively detects sense-for-sense translations.

Performance exceeds competitors by over two times in PlagDet score.

Abstract

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-OSA) for this task. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. Opposed to other methods, CL-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.