|
--- |
|
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 |
|
|
|
```python |
|
from datasets import load_dataset |
|
|
|
ds = load_dataset("AntiplagiatCompany/CL4Lang") |
|
``` |
|
|
|
## Create Index of collection |
|
|
|
```python |
|
# 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 |
|
|
|
```python |
|
# 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: |
|
|
|
```bibtex |
|
@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} |
|
} |
|
``` |
