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Cross-lingual Models of Word Embeddings: An Empirical Comparison
Despite interest in using cross-lingual knowledge to learn word embeddin...
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Methods for Detecting Paraphrase Plagiarism
Paraphrase plagiarism is one of the difficult challenges facing plagiari...
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Predicting risk of dyslexia with an online gamified test
Dyslexia is a specific learning disorder related to school failure. Dete...
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Refinement of Unsupervised Cross-Lingual Word Embeddings
Cross-lingual word embeddings aim to bridge the gap between high-resourc...
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Interactive Refinement of Cross-Lingual Word Embeddings
Cross-lingual word embeddings transfer knowledge between languages: mode...
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Learning to Represent Bilingual Dictionaries
Bilingual word embeddings have been widely used to capture the similarit...
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Clickbait detection using word embeddings
Clickbait is a pejorative term describing web content that is aimed at g...
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Detecting Cross-Lingual Plagiarism Using Simulated Word Embeddings
Cross-lingual plagiarism (CLP) occurs when texts written in one language are translated into a different language and used without acknowledgement. One of the most common methods used for detecting CLP requires online machine translators (such as Google or Microsoft translate) which are not always available, and given that plagiarism detection typically involves large document comparison, the amount of translations would overwhelm an online machine translator, especially when detecting plagiarism over the web. In addition, when translated texts are replaced with their synonyms, using online machine translators to detect CLP would result in poor performance. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a model that uses simulated word embeddings to reproduce the predictions of an online machine translator (Google translate) when detecting CLP without relying on online translators. The simulated embeddings comprise of translated words in different languages mapped in a common space, and replicated to increase the prediction probability of retrieving the translations of a word (and their synonyms) from the model. Unlike most existing models, the proposed model does not require parallel corpora, and accommodates multiple languages (multi-lingual). We demonstrated the effectiveness of the proposed model in detecting CLP in standard datasets that contain CLP cases, and evaluated its performance against a state-of-the-art baseline that relies on online machine translator (T+MA model). Evaluation results revealed that the proposed model is not only effective in detecting CLP, it outperformed the baseline. The results indicate that CLP could be detected with state-of-the-art performances by leveraging the prediction accuracy of an internet translator with word embeddings without relying on internet translators.
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