565 citations · 2.5k across the 94 of their papers we have counts for
5 papers · 1 filter
Synthetic and Natural Noise Both Break Neural Machine Translation
Yonatan Belinkov, Yonatan Bisk
Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfor…
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Yonatan Belinkov, James Glass
Neural models have become ubiquitous in automatic speech recognition systems. While neural networks are typically used as acoustic models in more complex systems, recent studies ha…
Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging
Hassan Sajjad, Fahim Dalvi, Nadir Durrani +3
Word segmentation plays a pivotal role in improving any Arabic NLP application. Therefore, a lot of research has been spent in improving its accuracy. Off-the-shelf tools, however,…
Neural Machine Translation Training in a Multi-Domain Scenario
Hassan Sajjad, Nadir Durrani, Fahim Dalvi +2
In this paper, we explore alternative ways to train a neural machine translation system in a multi-domain scenario. We investigate data concatenation (with fine tuning), model stac…
What do Neural Machine Translation Models Learn about Morphology?
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi +2
Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models lea…