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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Multi-Channel Encoder for Neural Machine Translation

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Hao Xiong

Baidu Inc.


Zhongjun He

Baidu Inc.


Xiaoguang Hu

Baidu Inc.


Hua Wu

Baidu Inc.


DOI:

10.1609/aaai.v32i1.11929


Abstract:

Attention-based Encoder-Decoder has the effective architecture for neural machine translation (NMT), which typically relies on recurrent neural networks (RNN) to build the blocks that will be lately called by attentive reader during the decoding process. This design of encoder yields relatively uniform composition on source sentence, despite the gating mechanism employed in encoding RNN. On the other hand, we often hope the decoder to take pieces of source sentence at varying levels suiting its own linguistic structure: for example, we may want to take the entity name in its raw form while taking an idiom as a perfectly composed unit. Motivated by this demand, we propose Multi-channel Encoder (MCE), which enhances encoding components with different levels of composition. More specifically, in addition to the hidden state of encoding RNN, MCE takes 1) the original word embedding for raw encoding with no composition, and 2) a particular design of external memory in Neural Turing Machine NTM) for more complex composition, while all three encoding strategies are properly blended during decoding. Empirical study on Chinese-English translation shows that our model can improve by 6.52 BLEU points upon a strong open source NMT system: DL4MT1. On the WMT14 English-French task, our single shallow system achieves BLEU=38.8, comparable with the state-of-the-art deep models.

Topics: AAAI

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HOW TO CITE:

Hao Xiong||Zhongjun He||Xiaoguang Hu||Hua Wu Multi-Channel Encoder for Neural Machine Translation Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Hao Xiong||Zhongjun He||Xiaoguang Hu||Hua Wu Multi-Channel Encoder for Neural Machine Translation AAAI 2018, .

Hao Xiong||Zhongjun He||Xiaoguang Hu||Hua Wu (2018). Multi-Channel Encoder for Neural Machine Translation. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Hao Xiong||Zhongjun He||Xiaoguang Hu||Hua Wu. Multi-Channel Encoder for Neural Machine Translation. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Hao Xiong||Zhongjun He||Xiaoguang Hu||Hua Wu. 2018. Multi-Channel Encoder for Neural Machine Translation. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Hao Xiong||Zhongjun He||Xiaoguang Hu||Hua Wu. (2018) "Multi-Channel Encoder for Neural Machine Translation", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Hao Xiong||Zhongjun He||Xiaoguang Hu||Hua Wu, "Multi-Channel Encoder for Neural Machine Translation", AAAI, p., 2018.

Hao Xiong||Zhongjun He||Xiaoguang Hu||Hua Wu. "Multi-Channel Encoder for Neural Machine Translation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Hao Xiong||Zhongjun He||Xiaoguang Hu||Hua Wu. "Multi-Channel Encoder for Neural Machine Translation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Hao Xiong||Zhongjun He||Xiaoguang Hu||Hua Wu. Multi-Channel Encoder for Neural Machine Translation. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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