The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Open accessAbstract
Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks.The neural machine translation models often consist of an encoder and a decoder.The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation.In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder-Decoder and a newly proposed gated recursive convolutional neural network.We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase.Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically.
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ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
Cited by
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Self-citationDeep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Self-citationInformer: 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
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228
Metadata
- Authors
- Cho, K., van Merrienboer, B., Bahdanau, D., & Bengio, Y.
- Venue
- Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation
- Published
- 2014
- Pages
- 103-111
- OpenAlex
- W2964199361
- Cited by
- 7,099
- References
- 1
- FWCI
- 116.81
- Type
- conference paper
- Language
- en
- Open access
- gold
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