192 citations · 244 across the 9 of their papers we have counts for
11 papers
Leveraging GPT-4 for Automatic Translation Post-Editing
Vikas Raunak, Amr Sharaf, Yiren Wang +2
While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify erro…
How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation
Amr Hendy, Mohamed Abdelrehim, Amr Sharaf +6
Generative Pre-trained Transformer (GPT) models have shown remarkable capabilities for natural language generation, but their performance for machine translation has not been thoro…
On Hard Episodes in Meta-Learning
Samyadeep Basu, Amr Sharaf, Nicolo Fusi +1
Existing meta-learners primarily focus on improving the average task accuracy across multiple episodes. Different episodes, however, may vary in hardness and quality leading to a w…
Semi-Supervised Few-Shot Intent Classification and Slot Filling
Samyadeep Basu, Karine lp Kiun Chong, Amr Sharaf +7
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data t…
Random Network Distillation as a Diversity Metric for Both Image and Text Generation
Liam Fowl, Micah Goldblum, Arjun Gupta +2
Generative models are increasingly able to produce remarkably high quality images and text. The community has developed numerous evaluation metrics for comparing generative models.…
Data Augmentation for Meta-Learning
Renkun Ni, Micah Goldblum, Amr Sharaf +2
Conventional image classifiers are trained by randomly sampling mini-batches of images. To achieve state-of-the-art performance, practitioners use sophisticated data augmentation s…
Active Imitation Learning with Noisy Guidance
Kianté Brantley, Amr Sharaf, Hal Daumé
Imitation learning algorithms provide state-of-the-art results on many structured prediction tasks by learning near-optimal search policies. Such algorithms assume training-time ac…
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…
Meta-Learning for Contextual Bandit Exploration
Amr Sharaf, Hal Daumé
We describe MELEE, a meta-learning algorithm for learning a good exploration policy in the interactive contextual bandit setting. Here, an algorithm must take actions based on cont…
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems
Amr Sharaf, Arpit Gupta, Hancheng Ge +2
In the slot-filling paradigm, where a user can refer back to slots in the context during the conversation, the goal of the contextual understanding system is to resolve the referri…
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
Amr Sharaf, Shi Feng, Khanh Nguyen +2
We describe the University of Maryland machine translation systems submitted to the WMT17 German-English Bandit Learning Task. The task is to adapt a translation system to a new do…