Murhaf Fares


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AraT5-MSAizer: Translating Dialectal Arabic to MSA
Murhaf Fares
Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024

This paper outlines the process of training the AraT5-MSAizer model, a transformer-based neural machine translation model aimed at translating five regional Arabic dialects into Modern Standard Arabic (MSA). Developed for Task 2 of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools, the model attained a BLEU score of 21.79% on the test set associated with this task.


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Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation
Murhaf Fares | Stephan Oepen | Erik Velldal
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun–noun compounds. Through a comprehensive series of experiments and in-depth error analysis, we show that transfer learning via parameter initialization and multi-task learning via parameter sharing can help a neural classification model generalize over a highly skewed distribution of relations. Further, we demonstrate how dual annotation with two distinct sets of relations over the same set of compounds can be exploited to improve the overall accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations.

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The 2018 Shared Task on Extrinsic Parser Evaluation: On the Downstream Utility of English Universal Dependency Parsers
Murhaf Fares | Stephan Oepen | Lilja Øvrelid | Jari Björne | Richard Johansson
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We summarize empirical results and tentative conclusions from the Second Extrinsic Parser Evaluation Initiative (EPE 2018). We review the basic task setup, downstream applications involved, and end-to-end results for seventeen participating teams. Based on in-depth quantitative and qualitative analysis, we correlate intrinsic evaluation results at different layers of morph-syntactic analysis with observed downstream behavior.


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Word vectors, reuse, and replicability: Towards a community repository of large-text resources
Murhaf Fares | Andrey Kutuzov | Stephan Oepen | Erik Velldal
Proceedings of the 21st Nordic Conference on Computational Linguistics


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A Dataset for Joint Noun-Noun Compound Bracketing and Interpretation
Murhaf Fares
Proceedings of the ACL 2016 Student Research Workshop