566 citations · 667 across the 12 of their papers we have counts for
13 papers
Self-Exploring Language Models: Active Preference Elicitation for Online Alignment
Shenao Zhang, Donghan Yu, Hiteshi Sharma +6
Preference optimization, particularly through Reinforcement Learning from Human Feedback (RLHF), has achieved significant success in aligning Large Language Models (LLMs) to adhere…
Deliberate then Generate: Enhanced Prompting Framework for Text Generation
Bei Li, Rui Wang, Junliang Guo +7
Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts. While existing…
Fast Vocabulary Projection Method via Clustering for Multilingual Machine Translation on GPU
Hossam Amer, Young Jin Kim, Mohamed Afify +2
Multilingual Neural Machine Translation has been showing great success using transformer models. Deploying these models is challenging because they usually require large vocabulary…
Building Multilingual Machine Translation Systems That Serve Arbitrary X-Y Translations
Akiko Eriguchi, Shufang Xie, Tao Qin +1
Multilingual Neural Machine Translation (MNMT) enables one system to translate sentences from multiple source languages to multiple target languages, greatly reducing deployment co…
Taming Sparsely Activated Transformer with Stochastic Experts
Simiao Zuo, Xiaodong Liu, Jian Jiao +5
Sparsely activated models (SAMs), such as Mixture-of-Experts (MoE), can easily scale to have outrageously large amounts of parameters without significant increase in computational…
Improving Multilingual Translation by Representation and Gradient Regularization
Yilin Yang, Akiko Eriguchi, Alexandre Muzio +3
Multilingual Neural Machine Translation (NMT) enables one model to serve all translation directions, including ones that are unseen during training, i.e. zero-shot translation. Des…
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…
Synthetic Data for Neural Machine Translation of Spoken-Dialects
Hany Hassan, Mostafa Elaraby, Ahmed Tawfik
In this paper, we introduce a novel approach to generate synthetic data for training Neural Machine Translation systems. The proposed approach transforms a given parallel corpus be…