14 citations · 16 across the 3 of their papers we have counts for
7 papers
Discovering Representation Sprachbund For Multilingual Pre-Training
Yimin Fan, Yaobo Liang, Alexandre Muzio +4
Multilingual pre-trained models have demonstrated their effectiveness in many multilingual NLP tasks and enabled zero-shot or few-shot transfer from high-resource languages to low…
Meta-Learning for Few-Shot NMT Adaptation
Amr Sharaf, Hany Hassan, Hal Daumé
We present META-MT, a meta-learning approach to adapt Neural Machine Translation (NMT) systems in a few-shot setting. META-MT provides a new approach to make NMT models easily adap…
Multi-Source Cross-Lingual Model Transfer: Learning What to Share
Xilun Chen, Ahmed Hassan Awadallah, Hany Hassan +2
Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of…
Achieving Human Parity on Automatic Chinese to English News Translation
Hany Hassan, Anthony Aue, Chang Chen +21
Machine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate acr…
Gender Aware Spoken Language Translation Applied to English-Arabic
Mostafa Elaraby, Ahmed Y. Tawfik, Mahmoud Khaled +2
Spoken Language Translation (SLT) is becoming more widely used and becoming a communication tool that helps in crossing language barriers. One of the challenges of SLT is the trans…
Universal Neural Machine Translation for Extremely Low Resource Languages
Jiatao Gu, Hany Hassan, Jacob Devlin +1
In this paper, we propose a new universal machine translation approach focusing on languages with a limited amount of parallel data. Our proposed approach utilizes a transfer-learn…