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20182021
most citedAdding Interpretable Attention to Neural Translation Models Improves Word Alignment

79 citations · 89 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.CL20201 cited

Enriched Annotations for Tumor Attribute Classification from Pathology Reports with Limited Labeled Data

Nick Altieri, Briton Park, Mara Olson +3

Precision medicine has the potential to revolutionize healthcare, but much of the data for patients is locked away in unstructured free-text, limiting research and delivery of effe…

cs.CL2020

A Streaming Approach For Efficient Batched Beam Search

Kevin Yang, Violet Yao, John DeNero +1

We propose an efficient batching strategy for variable-length decoding on GPU architectures. During decoding, when candidates terminate or are pruned according to heuristics, our s…

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.CL201979 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…