no code implementations • NAACL 2022 • Akiko Eriguchi, Shufang Xie, Tao Qin, Hany Hassan
Multilingual Neural Machine Translation (MNMT) enables one system to translate sentences from multiple source languages to multiple target languages, greatly reducing deployment costs compared with conventional bilingual systems.
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 • 29 May 2024 • Shenao Zhang, Donghan Yu, Hiteshi Sharma, ZiYi Yang, Shuohang Wang, Hany Hassan, Zhaoran Wang
Preference optimization, particularly through Reinforcement Learning from Human Feedback (RLHF), has achieved significant success in aligning Large Language Models (LLMs) to adhere to human intentions.
no code implementations • 31 May 2023 • Bei Li, Rui Wang, Junliang Guo, Kaitao Song, Xu Tan, Hany Hassan, Arul Menezes, Tong Xiao, Jiang Bian, Jingbo Zhu
Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts.
1 code implementation • ICLR 2022 • Simiao Zuo, Xiaodong Liu, Jian Jiao, Young Jin Kim, Hany Hassan, Ruofei Zhang, Tuo Zhao, Jianfeng Gao
While most on-going research focuses on improving SAMs models by exploring methods of routing inputs to experts, our analysis reveals that such research might not lead to the solution we expect, i. e., the commonly-used routing methods based on gating mechanisms do not work better than randomly routing inputs to experts.
1 code implementation • EMNLP 2021 • Yilin Yang, Akiko Eriguchi, Alexandre Muzio, Prasad Tadepalli, Stefan Lee, Hany Hassan
At the gradient level, we leverage a small amount of direct data (in thousands of sentence pairs) to regularize model gradients.
no code implementations • Findings (EMNLP) 2021 • Yimin Fan, Yaobo Liang, Alexandre Muzio, Hany Hassan, Houqiang Li, Ming Zhou, Nan Duan
Then we cluster all the target languages into multiple groups and name each group as a representation sprachbund.
no code implementations • WS 2020 • Amr Sharaf, Hany Hassan, Hal Daumé III
We frame the adaptation of NMT systems as a meta-learning problem, where we learn to adapt to new unseen domains based on simulated offline meta-training domain adaptation tasks.
no code implementations • WS 2019 • Lesly Miculicich, Marc Marone, Hany Hassan
In this paper, we report our system submissions to all 6 tracks of the WNGT 2019 shared task on Document-Level Generation and Translation.
no code implementations • WS 2019 • Young Jin Kim, Marcin Junczys-Dowmunt, Hany Hassan, Alham Fikri Aji, Kenneth Heafield, Roman Grundkiewicz, Nikolay Bogoychev
Taking our dominating submissions to the previous edition of the shared task as a starting point, we develop improved teacher-student training via multi-agent dual-learning and noisy backward-forward translation for Transformer-based student models.
no code implementations • WS 2019 • Ahmed Tawfik, Mahitab Emam, Khaled Essam, Robert Nabil, Hany Hassan
Parallel corpora available for building machine translation (MT) models for dialectal Arabic (DA) are rather limited.
1 code implementation • ACL 2019 • Xilun Chen, Ahmed Hassan Awadallah, Hany Hassan, Wei Wang, Claire Cardie
In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance.
Ranked #10 on Cross-Lingual NER on CoNLL Dutch
no code implementations • 27 Sep 2018 • Xilun Chen, Ahmed Hassan Awadallah, Hany Hassan, Wei Wang, Claire Cardie
In this work, we propose a zero-resource multilingual transfer learning model that can utilize training data in multiple source languages, while not requiring target language training data nor cross-lingual supervision.
2 code implementations • 15 Mar 2018 • Hany Hassan, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Shuangzhi Wu, Yingce Xia, Dong-dong Zhang, Zhirui Zhang, Ming Zhou
Machine translation has made rapid advances in recent years.
Ranked #3 on Machine Translation on WMT 2017 English-Chinese
no code implementations • 26 Feb 2018 • Mostafa Elaraby, Ahmed Y. Tawfik, Mahmoud Khaled, Hany Hassan, Aly Osama
One of the challenges of SLT is the translation from a language without gender agreement to a language with gender agreement such as English to Arabic.
no code implementations • NAACL 2018 • Jiatao Gu, Hany Hassan, Jacob Devlin, Victor O. K. Li
Our proposed approach utilizes a transfer-learning approach to share lexical and sentence level representations across multiple source languages into one target language.
no code implementations • IWSLT 2017 • Hany Hassan, Mostafa ElAraby, Ahmed Tawfik
Our approach is language independent and can be used to generate data for any variant of the source language such as slang or spoken dialect or even for a different language that is closely related to the source language.