A Multi-Hop Attention for RNN based Neural Machine Translation

no code implementations • WS 2019 • Shohei Iida, Ryuichiro Kimura, Hongyi Cui, Po-Hsuan Hung, Takehito Utsuro, Masaaki Nagata

Machine Translation Translation

Selecting Informative Context Sentence by Forced Back-Translation

no code implementations • WS 2019 • Ryuichiro Kimura, Shohei Iida, Hongyi Cui, Po-Hsuan Hung, Takehito Utsuro, Masaaki Nagata

Sentence Translation

Attention over Heads: A Multi-Hop Attention for Neural Machine Translation

no code implementations • ACL 2019 • Shohei Iida, Ryuichiro Kimura, Hongyi Cui, Po-Hsuan Hung, Takehito Utsuro, Masaaki Nagata

The first hop attention is the scaled dot-product attention which is the same attention mechanism used in the original Transformer.

Machine Translation Translation

Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017

no code implementations • WS 2017 • Zi Long, Ryuichiro Kimura, Takehito Utsuro, Tomoharu Mitsuhashi, Mikio Yamamoto

Long et al.(2017) proposed to select phrases that contain out-of-vocabulary words using the statistical approach of branching entropy.

Machine Translation NMT +2

Neural Machine Translation Model with a Large Vocabulary Selected by Branching Entropy

no code implementations • MTSummit 2017 • Zi Long, Ryuichiro Kimura, Takehito Utsuro, Tomoharu Mitsuhashi, Mikio Yamamoto

Neural machine translation (NMT), a new approach to machine translation, has achieved promising results comparable to those of traditional approaches such as statistical machine translation (SMT).

Machine Translation NMT +2