79 citations · 79 across the 1 of their papers we have counts for
3 papers
cs.CL2020
End-to-End Neural Word Alignment Outperforms GIZA++
Thomas Zenkel, Joern Wuebker, John DeNero
Word alignment was once a core unsupervised learning task in natural language processing because of its essential role in training statistical machine translation (MT) models. Alth…
cs.CL2019★ 79 cited
Adding Interpretable Attention to Neural Translation Models Improves Word Alignment
Thomas Zenkel, Joern Wuebker, John DeNero
Multi-layer models with multiple attention heads per layer provide superior translation quality compared to simpler and shallower models, but determining what source context is mos…
cs.CL2018
Compact Personalized Models for Neural Machine Translation
Joern Wuebker, Patrick Simianer, John DeNero
We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters…