Department of Computer Science

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Author: search.filters.author.Dr. Rao Muhammad Adeel NawabSubject: search.filters.subject.Sp19

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    Cross-Lingual Paraphrase Detection for English Urdu Language Pa
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Nida Waheed; SP19-RCS-025; LHR TP 7598; Dr. Rao Muhammad Adeel Nawab
    The main aim of the Cross-Lingual Paraphrase Detection (CLPD) task is to identify whether a text pair (text 01 in language A and text 02 in language B) are paraphrased of one another or not. With the massive increase in technological advancements in digital media and internet facilities, the availability of digital content across multiple languages has become free as well as easy to access. Besides paraphrasing, there has been a rise in cross-lingual paraphrasing since the development of advanced text processing tools. Moreover, the facility of translating existing texts from the original language into any other desired language using freely available translation tools has worsened the problems and triggered an increase in cross-lingual paraphrasing. Previously, there has been plenty of work done for the cross-lingual paraphrase detection in different languages but the downside is that under-resourced languages are still neglected. Urdu is a widely spoken language, despite the fact, the work done for the Urdu language in the CLPD domain is notably less because of the complexity and challenges of the Urdu language. It still comes in the category of low-resource languages. The research in the text analysis domain is mostly focused on well-resourced languages such as English and other European languages. The CLPD problem has formerly been explored for English-Czech, English German, English-Spanish, English Chinese, etc. language pairs. However, the CLPD problem has not yet been studied and reported for the English-Urdu language pair. Although, Cross-Lingual Paraphrasing is now being used enormously in various domains such as content writing, blog posts, and academics, especially in research works related to different languages. To overcome this research gap, a benchmark corpus and approaches are presented through this study for CLPD. The first objective of this research work is to develop a large benchmark Cross-Lingual Paraphrase Detection Corpus (CLPD-EU) for English-Urdu language pair at the sentence level by using a semi-automatic translation approach. For the creation of the CLPD-EU corpus, an existing corpus named Microsoft Research Paraphrase x Corpus (MRPC) is used. The second chief objective of this research work is to make a thorough comparison of classical machine learning (Bilingual Dictionary Based Approaches), Cross-lingual Word Embedding Based Approaches, Transfer Learning (Sentence Transformers Based Approaches) Approaches, and Feature Fusion Approaches on our proposed corpus. The results exhibited that our proposed Feature Fusion Approach 1 has shown notable performance for CLPD. The evaluation of all these approaches is carried out using the weighted average of Precision, Recall, and F1 scores.
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    Urdu Short Text Reuse Detection Using Sentence Transformers
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Hamza Hafeez; SP19-RCS-014; LHR TP 7589; Dr. Rao Muhammad Adeel Nawab
    The process of creating new text(s) using an existing one is called Text Reuse. The goal of text reuse detection is to identify whether a new text is created using a source text or not. A source text can be reused at different rewrite levels to generate either partially (Paraphrased) or fully (Verbatim) reused texts pairs. The Paraphrased Text Reuse shows the relationship between two text pairs conveying the same idea differently. Paraphrased text reuse text is difficult to detect since different edit operations can be used to generate it which varies considerably according to the context where it is being used. In literature, the Paraphrase Text Reuse Detection problem has been explored for English as well as some other European languages, but it has not been investigated for the Urdu language because of the non-availability of the Urdu paraphrase text reuse detection corpus. Therefore, to fulfill this gap this study aims to develop a large-scale gold standard benchmark Urdu Paraphrase Text Reuse Corpus (UPTRC) for the Urdu text reuse detection with special emphasizes on paraphrase text reuse detection for Urdu language. In addition, we have applied the baseline approaches (N-Gram Based Approaches) and proposed other approaches including Word Embedding Based Approaches, Sentence Transformers Based Approaches, and other Feature Fusion Approaches for Urdu Paraphrase Text Reuse Detection (UPTRD). However, Urdu Short Text Reuse Detection (USTRD) has been explored at the sentence level, passage level and document level along with different approaches that have been previously applied on Urdu Short Text Reuse Corpus (USTRC). Nonetheless, the USTRD task has only been explored with lexical and semantic approaches on USTRC but has not been explored with state-of-the-art Sentence Transformers Based Approaches and Word Embedding Based Approaches. The Sentence Transformers Based Approaches are used in a variety of applications and natural language processing tasks, especially for semantic text similarity and duplicate pair detection. But the Sentence Transformers has not been explored for Urdu text reuse detection. So, there is a need to explore the Sentence Transformers Based Approaches on the USTRC corpus for the task of USTRD. In this study, along with the corpus construction, we have applied the N-Gram Based Approaches as baseline and proposed new approaches including Word Embedding Based Approaches, Sentence Transformers Based Approaches, and Feature Fusion Approaches for our proposed UPTRC corpus. We have proposed applied compared and evaluated same approaches for USTRC corpus as well as our proposed corpus to investigate the performance of our proposed approaches. The best results obtained were the weighted average of F1 = 0.855607 by our proposed Feature Fusion Approaches for the task of UPTRD on our proposed UPTRC corpus. In the case of the USTRC corpus, our proposed Feature Fusion Approaches still outperformed the baseline approaches for ternary classification with F1 = 0.715164. The performance of the proposed approaches for both corpora is evaluated and reported using the weighted average of Precision (P), Recall (R), and F1 evaluation measures.