no code implementations • LREC 2022 • Shreyas Sharma, Kareem Darwish, Lucas Pavanelli, Thiago castro Ferreira, Mohamed Al-Badrashiny, Kamer Ali Yuksel, Hassan Sawaf
The performance of Machine Translation (MT) systems varies significantly with inputs of diverging features such as topics, genres, and surface properties.
no code implementations • OSACT (LREC) 2022 • Salaheddin Alzubi, Thiago castro Ferreira, Lucas Pavanelli, Mohamed Al-Badrashiny
Abusive speech on online platforms has a detrimental effect on users’ mental health.
1 code implementation • 20 Jan 2024 • Golara Javadi, Kamer Ali Yuksel, Yunsu Kim, Thiago castro Ferreira, Mohamed Al-Badrashiny
The findings suggest that NoRefER is not merely a tool for error detection but also a comprehensive framework for enhancing ASR systems' transparency, efficiency, and effectiveness.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
1 code implementation • 21 Jun 2023 • Kamer Ali Yuksel, Thiago Ferreira, Ahmet Gunduz, Mohamed Al-Badrashiny, Golara Javadi
The common standard for quality evaluation of automatic speech recognition (ASR) systems is reference-based metrics such as the Word Error Rate (WER), computed using manual ground-truth transcriptions that are time-consuming and expensive to obtain.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 20 Jun 2023 • Kamer Ali Yuksel, Ahmet Gunduz, Mohamed Al-Badrashiny, Shreyas Sharma, Hassan Sawaf
The online learning capability of this system allows for dynamic adaptation to alterations in the domain or machine translation engines, thereby obviating the necessity for additional training.
no code implementations • LREC 2016 • Mona Diab, Mahmoud Ghoneim, Abdelati Hawwari, Fahad AlGhamdi, Nada Almarwani, Mohamed Al-Badrashiny
We present our effort to create a large Multi-Layered representational repository of Linguistic Code-Switched Arabic data.
no code implementations • WS 2017 • Mohamed Al-Badrashiny, Abdelati Hawwari, Mona Diab
In this paper we present a system for automatic Arabic text diacritization using three levels of analysis granularity in a layered back off manner.
no code implementations • WS 2016 • Mohamed Al-Badrashiny, Abdelati Hawwari, Mahmoud Ghoneim, Mona Diab
We propose an automated method that identifies the morphological and syntactic flexibility of Arabic Verbal Multiword Expressions (AVMWE).
no code implementations • WS 2016 • Maryam Aminian, Mohamed Al-Badrashiny, Mona Diab
We present an approach for automatic verification and augmentation of multilingual lexica.
no code implementations • COLING 2016 • Mohamed Al-Badrashiny, Mona Diab
We introduce a generic Language Independent Framework for Linguistic Code Switch Point Detection.
no code implementations • LREC 2016 • Mohamed Al-Badrashiny, Arfath Pasha, Mona Diab, Nizar Habash, Owen Rambow, Wael Salloum, Esk, Ramy er
Text preprocessing is an important and necessary task for all NLP applications.
no code implementations • LREC 2014 • Mona Diab, Mohamed Al-Badrashiny, Maryam Aminian, Mohammed Attia, Heba Elfardy, Nizar Habash, Abdelati Hawwari, Wael Salloum, Pradeep Dasigi, Esk, Ramy er
Multiple levels of quality checks are performed on the output of each step in the creation process.
no code implementations • LREC 2014 • Arfath Pasha, Mohamed Al-Badrashiny, Mona Diab, Ahmed El Kholy, Esk, Ramy er, Nizar Habash, Manoj Pooleery, Owen Rambow, Ryan Roth
In this paper, we present MADAMIRA, a system for morphological analysis and disambiguation of Arabic that combines some of the best aspects of two previously commonly used systems for Arabic processing, MADA (Habash and Rambow, 2005; Habash et al., 2009; Habash et al., 2013) and AMIRA (Diab et al., 2007).