E nglish- A rabic Cross-language Plagiarism Detection (2021)

Abstract: The advancement of the web and information technology has contributed to the rapid growth of digital libraries and automatic machine translation tools which easily translate texts from one language into another. These have increased the content accessible in different languages, which results in easily performing translated plagiarism, which are referred to as “cross-language plagiarism”. Recognition of plagiarism among texts in different languages is more challenging than identifying plagiarism within a corpus written in the same language. This paper proposes a new technique for enhancing English-Arabic cross-language plagiarism detection at the sentence level. This technique is based on semantic and syntactic feature extraction using word order, word embedding and word alignment with multilingual encoders. Those features, and their combination with different machine learning (ML) algorithms, are then used in order to aid the task of classifying sentences as either plagiarized or non-plagiarized. The proposed approach has been deployed and assessed using datasets presented at SemEval-2017. Analysis of experimental data demonstrates that utilizing extracted features and their combinations with various ML classifiers achieves promising results.

## introduction the advancement of the internet and information technology have expanded rapidly the availability of digital libraries and automatic machine translation tools, which facilitate translating a text from one language to another language. this has caused the number of cases of translated plagiarism, referred to as "cross-language plagiarism", to perform substantially. it is a type of plagiarism that occurs when textual content is translated into another language without giving acknowledgment of original sources. this type of plagiarism is more difficult to detect since each language has its own structure. several plagiarism detection techniques have been proposed to address monolingual plagiarism, that identify plagiarism instances written in the same language. however, there have been few studies that concentrate on researching and developing methods for identifying cross-language (and in particular english-arabic) plagiarism. these techniques cannot effectively detect more extensively disguised cases of cross-language plagiarism. @xcite observed that existing techniques have difficulty detecting linguistic modifications like replacing words and phrases by synonyms. when a text is translated from arabic into english, synonyms are introduced after the translation, thus it is difficult to identify plagiarism. this paper proposes a new approach for enhancing english-arabic cross-language plagiarism detection at the sentence level. this technique is based on semantic and syntactic feature extraction using word alignment, word order, word embedding and multilingual encoder models. we investigate the effectiveness of using those features and their combination with different machine learning (ml) algorithms for classifying sentences as either plagiarized or non-plagiarized. the rest of this paper is structured as follows: section 2 presents a review of related work on cross-language plagiarism detection techniques. in section 3 we illustrate the proposed approach. the experimental results and discussion are provided in section 4. finally, section 5 concludes and presents future work. ## related work a number of studies have been scrutinized crosslanguage plagiarism detection. @xcite presented a classification of cross-language similarity detection methods which was subsequently developed by @xcite . these approaches were classified on the basis of the mechanism used for detecting similarity as shown in figure 1 . for an instance, cross-language character n-grams (cl-cng), which were presented by @xcite , segmenting texts into n-grams for performing comparisons between pairs of texts and measuring the similarity without translation. another study was on the basis of comparable corpora and was presented by @xcite , who introduced the cross-language explicit semantic analysis (cl-esa) model that were used wikipedia for computing the similarity between pairs of documents in different languages. presented a machine translation model to convert texts into the common language followed by employing a monolingual analysis. some published research has focused on english-arabic cross-language plagiarism detection. for example, aljohani and mohd (2014) proposed an english-arabic cross-language detection approach based on google translate to translate the texts and applying a winnowing algorithm, which proposed by @xcite . another study presented by @xcite proposed a technique based on latent semantic indexing (lsi) and parallel corpora to build a cross-language semantic vector space to compute similarity of the context. @xcite used a logistic regression classifier based on longest common subsequence and cosine similarity measurements and n-gram features at keyphrase level. a study utilized semantic metrics and wordnet for gauging the degree of semantic similarity between words and used it to calculate the similarity for texts and paragraphs of english-french and english-arabic plagiarism instances @xcite . @xcite employed a fuzzy semantic approach to identify cross-language plagiarism cases employing @xcite similarity metrics and wordnet to compute semantic similarity between words. based on this review, we have only identified a few studies which have attempted to detect crosslanguage plagiarism in the english-arabic domain. most of these studies have tried to identify plagiarism based on semantic features and key phrases. to the best of our knowledge, none of these studies has tried to detect plagiarism using english-arabic pairs based on sentence level analysis, nor has any integrated semantic and syntactic features using word embedding and word alignment features with multilingual encoder models. ## proposed method the key idea of the proposed plagiarism detection technique for english-arabic pairs of sentence is formulated as a classification task, which classifies each pair of sentences as either plagiarized or nonplagiarized. in order to tackle this problem, it is necessary to analyze texts using different features extracted at syntactic and semantic levels. thus, we propose methods based on word embedding, word order and word alignment with multilingual figure 1 : classification of various techniques for cross-language similarity analysis @xcite . ## experiments and results to assess the performance of the proposed techniques, we have conducted experiments to examine the impact of both individual and combined features used to train each classifier. for evaluating the performance of the classifiers, we have used 10-fold cross-validation and the f1measure (f1 score), which is the harmonic average of precision and recall, as shown in equation 10 . the experiments have been carried out using python with the scikit-learn library to build each classifier, and we have used the grid search method to find the best values of hyperparameters for configuration of the ml models. ## conclusion in this paper we introduced a technique based on analyzing sentences using syntactic and semantic features with ml classifiers to detect english-arabic cross-lingual plagiarism. the features we used involve word order, word embedding and word alignment with multilingual encoders. we also explored the effects of using extracted features and their combinations along with the different classifiers. the proposed method has been assessed by using a compilation of four datasets. according to the evaluation, the integration of combined extracted features with the classifiers demonstrates improved performance. overall, the svc classifier based on combination of all features accomplishes the best results with the f1score of 0.879. in future work, the approach will be expanded to include use of neural network techniques. references @xcite . arabic-english cross-language plagiarism detection using