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.CL2017
Comparison of Decoding Strategies for CTC Acoustic Models
Thomas Zenkel, Ramon Sanabria, Florian Metze +4
Connectionist Temporal Classification has recently attracted a lot of interest as it offers an elegant approach to building acoustic models (AMs) for speech recognition. The CTC lo…