The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Recurrent Continuous Translation Models
Open accessAbstract
We introduce a class of probabilistic continuous translation models called Recurrent Continuous Translation Models that are purely based on continuous representations for words, phrases and sentences and do not rely on alignments or phrasal translation units.The models have a generation and a conditioning aspect.The generation of the translation is modelled with a target Recurrent Language Model, whereas the conditioning on the source sentence is modelled with a Convolutional Sentence Model.Through various experiments, we show first that our models obtain a perplexity with respect to gold translations that is > 43% lower than that of stateof-the-art alignment-based translation models.Secondly, we show that they are remarkably sensitive to the word order, syntax, and meaning of the source sentence despite lacking alignments.Finally we show that they match a state-of-the-art system when rescoring n-best lists of translations.
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Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
The mathematics of statistical machine translation: parameter estimation
Brown, P. F., Pietra, V. J. D., Pietra, S. A. D., & Mercer, R. L. (1993) · Cited by 3,973
Extensions of recurrent neural network language model
Mikolov, T., Kombrink, S., Burget, L., Cernocky, J., & Khudanpur, S. (2011) · 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2011.5947611 · Cited by 1,568
Generating Text with Recurrent Neural Networks
Sutskever, I., Martens, J., & Hinton, G. E. (2011) · Cited by 1,264
Semantic Compositionality through Recursive Matrix-Vector Spaces
Socher, R., Huval, B., Manning, C. D., & Ng, A. Y. (2012) · Cited by 1,259
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
Socher, R., Huang, E., Pennin, J., Manning, C. D., & Ng, A. Y. (2011) · Cited by 782
A Simple, Fast, and Effective Reparameterization of IBM Model 2
Dyer, C., Chahuneau, V., & Smith, N. A. (2018) · doi:10.1184/r1/6472985 · Cited by 767
Context dependent recurrent neural network language model
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Cited by
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
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
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
“In (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014; Cho et al., 2014; Luong et al., 2015), the source representation s is only used once to initialize the decoder hidden state.”
BackgroundMethodShow, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
Metadata
- Authors
- Kalchbrenner, N., & Blunsom, P.
- Venue
- Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing
- Published
- 2013
- Pages
- 1700-1709
- OpenAlex
- W1753482797
- Cited by
- 1,232
- References
- 17
- FWCI
- 50.95
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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