Search Results for author: Mohamed Al-Badrashiny

Found 20 papers, 2 papers with code

MTLens: Machine Translation Output Debugging

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.

Benchmarking Machine Translation +2

A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-Supervision

1 code implementation21 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

EvolveMT: an Ensemble MT Engine Improving Itself with Usage Only

no code implementations20 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.

Machine Translation Sentence +1

A Layered Language Model based Hybrid Approach to Automatic Full Diacritization of Arabic

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.

Arabic Text Diacritization Language Modelling +3

Automatic Verification and Augmentation of Multilingual Lexicons

no code implementations WS 2016 Maryam Aminian, Mohamed Al-Badrashiny, Mona Diab

We present an approach for automatic verification and augmentation of multilingual lexica.

MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic

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).

Chunking Lemmatization +5

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