The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Massive Exploration of Neural Machine Translation Architectures
Open accessAbstract
Neural Machine Translation (NMT) has shown remarkable progress over the past few years, with production systems now being deployed to end-users.As the field is moving rapidly, it has become unclear which elements of NMT architectures have a significant impact on translation quality.In this work, we present a large-scale analysis of the sensitivity of NMT architectures to common hyperparameters.We report empirical results and variance numbers for several hundred experimental runs, corresponding to over 250,000 GPU hours on a WMT English to German translation task.Our experiments provide practical insights into the relative importance of factors such as embedding size, network depth, RNN cell type, residual connections, attention mechanism, and decoding heuristics.As part of this contribution, we also release an open-source NMT framework in TensorFlow to make it easy for others to reproduce our results and perform their own experiments.
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Self-citationNeural Machine Translation by Jointly Learning to Align and Translate
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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
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A review on the attention mechanism of deep learning
Niu, Z., Zhong, G., & Yu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.091 · Cited by 3,379
Deformable ConvNets V2: More Deformable, Better Results
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Attention gated networks: learning to leverage salient regions in medical images
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Metadata
- Authors
- Britz, D., Goldie, A., Luong, M.-T., & Le, Q.
- Venue
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Published
- 2017
- Pages
- 1442-1451
- OpenAlex
- W2594990650
- Cited by
- 482
- References
- 30
- FWCI
- 32.27
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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