Amr Sharaf

Data Augmentation for Meta-Learning

2020 · arXiv (Cornell University)

Conventional image classifiers are trained by randomly sampling mini-batches of images. To achieve state-of-the-art performance, practitioners use sophisticated data augmentation schemes to expand the amount of training data available for sampling. In contrast, meta-learning algorithms …

Leveraging GPT-4 for Automatic Translation Post-Editing

2023 · arXiv (Cornell University)

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 errors and enhance quality under critical settings. In this work, we …

How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation

2023 · arXiv (Cornell University)

Generative Pre-trained Transformer (GPT) models have shown remarkable capabilities for natural language generation, but their performance for machine translation has not been thoroughly investigated. In this paper, we present a comprehensive evaluation of GPT models …