8 citations · 11 across the 4 of their papers we have counts for
8 papers
Training Mixed-Domain Translation Models via Federated Learning
Peyman Passban, Tanya Roosta, Rahul Gupta +2
Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques. In this work, we leverage federated learning…
Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMax
Ehsan Kamalloo, Mehdi Rezagholizadeh, Peyman Passban +1
In Natural Language Processing (NLP), finding data augmentation techniques that can produce high-quality human-interpretable examples has always been challenging. Recently, leverag…
Robust Embeddings Via Distributions
Kira A. Selby, Yinong Wang, Ruizhe Wang +4
Despite recent monumental advances in the field, many Natural Language Processing (NLP) models still struggle to perform adequately on noisy domains. We propose a novel probabilist…
ALP-KD: Attention-Based Layer Projection for Knowledge Distillation
Peyman Passban, Yimeng Wu, Mehdi Rezagholizadeh +1
Knowledge distillation is considered as a training and compression strategy in which two neural networks, namely a teacher and a student, are coupled together during training. The…
Revisiting Robust Neural Machine Translation: A Transformer Case Study
Peyman Passban, Puneeth S. M. Saladi, Qun Liu
Transformers (Vaswani et al., 2017) have brought a remarkable improvement in the performance of neural machine translation (NMT) systems but they could be surprisingly vulnerable t…
Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers
Yimeng Wu, Peyman Passban, Mehdi Rezagholizade +1
With the growth of computing power neural machine translation (NMT) models also grow accordingly and become better. However, they also become harder to deploy on edge devices due t…