@inproceedings{wang-2019-towards,
title = "Towards Linear Time Neural Machine Translation with Capsule Networks",
author = "Wang, Mingxuan and
Xie, Jun and
Tan, Zhixing and
Su, Jinsong and
Xiong, Deyi and
Li, Lei",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1074",
doi = "10.18653/v1/D19-1074",
pages = "803--812",
abstract = "In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as CapsNMT. CapsNMT uses an aggregation mechanism to map the source sentence into a matrix with pre-determined size, and then applys a deep LSTM network to decode the target sequence from the source representation. Unlike the previous work (CITATION) to store the source sentence with a passive and bottom-up way, the dynamic routing policy encodes the source sentence with an iterative process to decide the credit attribution between nodes from lower and higher layers. CapsNMT has two core properties: it runs in time that is linear in the length of the sequences and provides a more flexible way to aggregate the part-whole information of the source sentence. On WMT14 English-German task and a larger WMT14 English-French task, CapsNMT achieves comparable results with the Transformer system. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for sequence to sequence problems.",
}
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<abstract>In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as CapsNMT. CapsNMT uses an aggregation mechanism to map the source sentence into a matrix with pre-determined size, and then applys a deep LSTM network to decode the target sequence from the source representation. Unlike the previous work (CITATION) to store the source sentence with a passive and bottom-up way, the dynamic routing policy encodes the source sentence with an iterative process to decide the credit attribution between nodes from lower and higher layers. CapsNMT has two core properties: it runs in time that is linear in the length of the sequences and provides a more flexible way to aggregate the part-whole information of the source sentence. On WMT14 English-German task and a larger WMT14 English-French task, CapsNMT achieves comparable results with the Transformer system. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for sequence to sequence problems.</abstract>
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%0 Conference Proceedings
%T Towards Linear Time Neural Machine Translation with Capsule Networks
%A Wang, Mingxuan
%A Xie, Jun
%A Tan, Zhixing
%A Su, Jinsong
%A Xiong, Deyi
%A Li, Lei
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F wang-2019-towards
%X In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as CapsNMT. CapsNMT uses an aggregation mechanism to map the source sentence into a matrix with pre-determined size, and then applys a deep LSTM network to decode the target sequence from the source representation. Unlike the previous work (CITATION) to store the source sentence with a passive and bottom-up way, the dynamic routing policy encodes the source sentence with an iterative process to decide the credit attribution between nodes from lower and higher layers. CapsNMT has two core properties: it runs in time that is linear in the length of the sequences and provides a more flexible way to aggregate the part-whole information of the source sentence. On WMT14 English-German task and a larger WMT14 English-French task, CapsNMT achieves comparable results with the Transformer system. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for sequence to sequence problems.
%R 10.18653/v1/D19-1074
%U https://aclanthology.org/D19-1074
%U https://doi.org/10.18653/v1/D19-1074
%P 803-812
Markdown (Informal)
[Towards Linear Time Neural Machine Translation with Capsule Networks](https://aclanthology.org/D19-1074) (Wang et al., EMNLP-IJCNLP 2019)
ACL
- Mingxuan Wang, Jun Xie, Zhixing Tan, Jinsong Su, Deyi Xiong, and Lei Li. 2019. Towards Linear Time Neural Machine Translation with Capsule Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 803–812, Hong Kong, China. Association for Computational Linguistics.