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Neural machine translation

Neural machine translation

A comprehensive approach to automated translation that uses large neural networks to maximize translation performance.

Neural machine translation (NMT) is an approach to machine translation that attempts to build and train a large neural network that reads a sentence and outputs a correct translation. Contrary to the traditional phrase-based machine translation system which is composed of different small sub-components that are tuned separately.

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An Empirical Study of Adequate Vision Span for Attention-Based Neural Machine Translation

Raphael Shu, Hideki Nakayama

Academic paper

Compression of Neural Machine Translation Models via Pruning

Abigail See, Minh-Thang Luong, Christopher D. Manning

Academic paper

Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation

Junyang Lin, Shuming Ma, Qi Su, Xu Sun

Academic paper

English-Catalan Neural Machine Translation in the Biomedical Domain through the cascade approach

Marta R. Costa-jussà, Noe Casas, Maite Melero

Academic paper

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean

Academic paper

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey,Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser,Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens,George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa,Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes and Jeffrey Dean

Academic paper

Mutual Information and Diverse Decoding Improve Neural Machine Translation

Jiwei Li, Dan Jurafsky

Academic paper

Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

Andrea F. Daniele, Mohit Bansal, Matthew R. Walter

Academic paper

Neural Machine Translation by Jointly Learning to Align and Translate

Dzmitry Bahdanau, Kyung Hyun Cho and Yoshua Bengio

Academic paper

OpenNMT: Open-Source Toolkit for Neural Machine Translation

Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander M. Rush

Academic paper

Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations

Upol Ehsan, Brent Harrison, Larry Chan, Mark O. Riedl

Academic paper

Refining Source Representations with Relation Networks for Neural Machine Translation

Wen Zhang, Jiawei Hu, Yang Feng, Qun Liu

Academic paper

Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback

Khanh Nguyen, Hal Daumé III, Jordan Boyd-Graber

Academic paper

Search Engine Guided Non-Parametric Neural Machine Translation

Jiatao Gu, Yong Wang, Kyunghyun Cho, Victor O. K. Li

Academic paper

The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task

Amr Sharaf, Shi Feng, Khanh Nguyen, Kianté Brantley, Hal Daumé III

Academic paper

Towards Decoding as Continuous Optimization in Neural Machine Translation

Cong Duy Vu Hoang University of Melbourne, Gholamreza Haffari Monash University, Trevor Cohn University of Melbourne

Academic paper

Towards Neural Machine Translation with Latent Tree Attention

James Bradbury, Richard Socher

Academic paper

Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic

Taro Sekiyama, Akifumi Imanishi, Kohei Suenaga

Academic paper

Unsupervised Neural Machine Translation

Mikel Artetxe, Gorka Labaka, Eneko Agirre, Kyunghyun Cho

Academic paper

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