Papers by Slim Abdennadher

6 papers
Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text (2023.eacl-main)

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Challenge: Code-switching (CS) is a problem in machine translation, but its performance is not investigated for CS settings.
Approach: They propose to use morphological segmentation techniques for machine translation tasks . they compare morphology-based and frequency-based segmentation for MT tasks based on data size .
Outcome: The proposed approach performs best in MT tasks but under-performs in other languages.
A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions (2025.coling-main)

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Challenge: Code-switching (CSW) is a common linguistic phenomenon in multilingual societies . current literature on CSW in the arab world is limited to the Arabic language .
Approach: They present a review of the literature in the field of code-switched Arabic NLP . they propose recommendations for future research .
Outcome: This review provides a broad perspective on the current literature in the field of code-switched Arabic NLP . it also provides recommendations for future research .
Collection and Analysis of Code-switch Egyptian Arabic-English Speech Corpus (L18-1)

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Challenge: despite of the great demand, there is still a huge shortage in available corpora for dialectal languages and code-switched speech.
Approach: They collect conversational Egyptian Arabic spontaneous speech, extract transcriptions and analyze it from a code-switching perspective.
Outcome: The authors collect conversational Egyptian Arabic spontaneous speech, extract transcriptions and analyze speech from the code-switching perspective.
Cairo Student Code-Switch (CSCS) Corpus: An Annotated Egyptian Arabic-English Corpus (2020.lrec-1)

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Challenge: Code-switching is a phenomenon commonly observed in the Arabicspeaking world . there is still a huge gap in the available resources and NLP applications .
Approach: They propose a corpus of Egyptian- Arabic code-switch speech data that is fully tokenized, lemmatized and annotated for part-of-speech tags.
Outcome: The proposed corpus of Egyptian- Arabic code-switch speech data is fully tokenized, lemmatized and annotated for part-of-speech tags.
Enhancing Deep Learning with Embedded Features for Arabic Named Entity Recognition (2022.lrec-1)

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Challenge: Word embeddings can capture the semantics of words and other hidden features, but the Arabic language is complex and requires a large amount of information to process.
Approach: They propose to add morphological and syntactical features to Arabic word embeddings to train the model.
Outcome: The proposed model outperforms the previous systems to the best of our knowledge.
ArzEn: A Speech Corpus for Code-switched Egyptian Arabic-English (2020.lrec-1)

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Challenge: a corpus of Arabic-English code-switching (CS) spontaneous speech is collected in an Egyptian university soundproof room . the language in Egypt is rather complex and poses many challenges to natural language processing (NLP)
Approach: They present an Egyptian Arabic-English code-switching (CS) spontaneous speech corpus.
Outcome: The proposed corpus is designed to be used in automatic speech recognition systems . it provides a useful resource for analyzing the CS phenomenon from linguistic, sociological, and psychological perspectives.

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