no code implementations • AMTA 2022 • Muhammad N ElNokrashy, Amr Hendy, Mohamed Maher, Mohamed Afify, Hany Hassan
In a WMT-based setting, we see 1. 3 and 0. 4 BLEU points improvement for the zero-shot setting, and when using direct data for training, respectively, while from-English performance improves by 4. 17 and 0. 85 BLEU points.
1 code implementation • 18 Feb 2023 • Amr Hendy, Mohamed Abdelrehim, Amr Sharaf, Vikas Raunak, Mohamed Gabr, Hitokazu Matsushita, Young Jin Kim, Mohamed Afify, Hany Hassan Awadalla
In this paper, we present a comprehensive evaluation of GPT models for machine translation, covering various aspects such as quality of different GPT models in comparison with state-of-the-art research and commercial systems, effect of prompting strategies, robustness towards domain shifts and document-level translation.
no code implementations • 18 Oct 2022 • Amr Hendy, Mohamed Abdelghaffar, Mohamed Afify, Ahmed Y. Tawfik
This paper presents Domain-Specific Sub-network (DoSS).
no code implementations • AMTA 2022 • Hossam Amer, Young Jin Kim, Mohamed Afify, Hitokazu Matsushita, Hany Hassan Awadallah
The proposed method speeds up the vocab projection step itself by up to 2. 6x.
no code implementations • 11 Aug 2022 • Muhammad ElNokrashy, Amr Hendy, Mohamed Maher, Mohamed Afify, Hany Hassan Awadalla
In a WMT evaluation campaign, From-English performance improves by 4. 17 and 2. 87 BLEU points, in the zero-shot setting, and when direct data is available for training, respectively.
no code implementations • WMT (EMNLP) 2021 • Amr Hendy, Esraa A. Gad, Mohamed Abdelghaffar, Jailan S. ElMosalami, Mohamed Afify, Ahmed Y. Tawfik, Hany Hassan Awadalla
This paper describes our submission to the constrained track of WMT21 shared news translation task.
no code implementations • WMT (EMNLP) 2020 • Muhammad N. ElNokrashy, Amr Hendy, Mohamed Abdelghaffar, Mohamed Afify, Ahmed Tawfik, Hany Hassan Awadalla
For the mBART finetuning setup, provided by the organizers, our method shows 7% and 5% relative improvement over baseline, in sacreBLEU score on the test set for Pashto and Khmer respectively.
no code implementations • 26 Jun 2018 • Mohamed Adel, Mohamed Afify, Akram Gaballah
The d-vectors, generated from a feed forward deep neural network trained to distinguish between speakers, are used as features to perform alignment and hence calculate the overall distance between the enrolment and test utterances. We present results on the NIST 2008 data set for speaker verification where the proposed method outperforms the conventional i-vector baseline with PLDA scores and outperforms d-vector approach with local distances based on cosine and PLDA scores.