The DOI matches its Crossref record; 17 of the 18 references it cites were found in a registry or an index.
Effective Approaches to Attention-based Neural Machine Translation
Open accessAbstract
An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation.However, there has been little work exploring useful architectures for attention-based NMT.This paper examines two simple and effective classes of attentional mechanism: a global approach which always attends to all source words and a local one that only looks at a subset of source words at a time.We demonstrate the effectiveness of both approaches on the WMT translation tasks between English and German in both directions.With local attention, we achieve a significant gain of 5.0 BLEU points over non-attentional systems that already incorporate known techniques such as dropout.Our ensemble model using different attention architectures yields a new state-of-the-art result in the WMT'15 English to German translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over the existing best system backed by NMT and an n-gram reranker.1
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“Since then, various enhancements have been proposed, such as local attention (Luong et al. 2015), supervised attention (Mi et al. 2016; Liu et al. 2016), hierarchical attention (Zhao et al. 2018), and self-attention (Vaswani et al. 2017; Yang et al. 2018).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Luong et al. 2015
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Cited by
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A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17325 · Cited by 7,752
Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1355 · Cited by 3,684
A review on the attention mechanism of deep learning
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Metadata
- Authors
- Luong, T., Pham, H., & Manning, C. D.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
- Published
- 2015
- Pages
- 1412-1421
- OpenAlex
- W1902237438
- Cited by
- 8,382
- References
- 18
- Citations by year
- 2026: 177 · 2025: 454 · 2024: 586 · 2023: 839 · 2022: 985 · 2021: 1356 · 2020: 1354 · 2019: 1372 · 2018: 783 · 2017: 337 · 2016: 133 · 2015: 5
- FWCI
- 236.52
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- Bloomberg L.P.
- Nvidia (Tesla K40)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- attention mechanism, neural machine translation, local attention, global attention, English-German translation, ensemble learning, dropout
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