metadata

license: apache-2.0
language:
  - ru
  - hy
  - es
  - en
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: collection
        path:
          - collection.csv
      - split: query
        path:
          - query.csv
tags:
  - paraphrase
  - crosslingual

Cross-lingual plagiarism detection: Two are better than one

The widespread availability of scientific documents in multiple languages, coupled with the development of automatic translation and editing tools, has created a demand for efficient methods that can detect plagiarism across different languages.

A dataset for cross-lingual plagiarism evaluation. Collection consists of a subset of Wikipedia articles on 4 languages (ru, hy, es, en). Quary consists of wikipedia documents in each of the four languages with translated sentences with Google Translate API from collection, and also XML-markup for them.

Usage of Dataset

Load Data

from datasets import load_dataset

ds = load_dataset("AntiplagiatCompany/CL4Lang")

Create Index of collection

# The list consists of dictionaries with document id, text of the document and text language information (also present xml data, but it used only for querys, not for indexing)
collection = ds['collection'].to_list()

# The list of object can be indexing by using different methods (vector search methods or classical BM25 indexing methods)
index = make_index(collection)

Evaluate The Query Result

# The list consists of dictionaries with document id, text of the document, text language information, and XML information about text reuses in query from collection.
queries = ds['query'].to_list()

real, predict = [], []
for query in queries:
    real.append(query['xml'])
    predict.append(
        convert_answer_to_xml(
            index.search(text=query['text'], lang=query['lang'])
        )
    )

# More information about the XML markup description and evaluation see http://pan.webis.de/clef13/pan13-web/plagiarism-detection.html
evaluate_system(real, predict)

Citation

If you use that results in your research, please cite our paper:

@article{10.1134/S0361768823040138,
    author = {Avetisyan, K. and Gritsay, G. and Grabovoy, A.},
    title = {Cross-Lingual Plagiarism Detection: Two Are Better Than One},
    year = {2023},
    issue_date = {Aug 2023},
    publisher = {Plenum Press},
    address = {USA},
    volume = {49},
    number = {4},
    issn = {0361-7688},
    url = {https://doi.org/10.1134/S0361768823040138},
    doi = {10.1134/S0361768823040138},
    journal = {Program. Comput. Softw.},
    month = aug,
    pages = {346–354},
    numpages = {9},
    keywords = {cross-lingual plagiarism detection, cross-lingual plagiarism detection benchmark, under-resourced languages, sequential merger approach}
}