Chunk-based Decoder for Neural Machine Translationynaga/papers/ishiwatari-acl...Chunk-based Decoder for Neural Machine Translation Shonosuke Ishiwatari¹, Jingtao Yao², Shujie Liu³,

Chunk-based Decoder for Neural Machine TranslationShonosuke Ishiwatari¹, Jingtao Yao², Shujie Liu³, Mu Li³, Ming Zhou³, Naoki Yoshinaga⁴, Masaru Kitsuregawa⁴⁵, Weijia Jia²

¹The University of Tokyo ²Shanghai Jiao Tong University ³Microsoft Research Asia

⁴Institute of Industrial Science, the University of Tokyo ⁵National Institute of Informatics

Overview

Proposal: Chunk-based Decoder for Neural Machine Translation

Translation examples

Idea: Using a chunk rather than a word as basic translation unit

SOTA performance in distant languages (En -> Ja)

Translation between distant language pairs are difficult

Word-based Encoder-Decoder

か が 犬だれ

!""

!"# $%&&'( $)*+,'+(' " -+.

＋ * α(4, dog)

!""

/&&'(&%+(

…

0'1+-'2

3(1+-'2

Difficulties of translation of En -> Ja

Experiments

Our decoder for NMT

Quality of translationQuality of generated chunks

※Scores inside () are not our implementations

Data - ASPEC [Nakazawa+ 16], 1.6M En/Ja pairs

Preprocessing - Bunsetsu chunking with J.DepP [Yoshinaga & Kitsuregawa 09]

Baseline systems 1. Word-based encoder-decoder [Bahdanau+ 15] 2. Tree-based encoder [Eriguchi+ 16] (SOTA)

Results

I heard that someone was bitten by a dog, weren’ t you injured?だれ か が / 犬 に / 噛ま れ た そう だ けれど、/ 君　は / 怪我 し なかっ た ？

Japanese sentence has - longer sequence (En: 25 vs. Ja: 30 [words/sentence]) - free chunk order (e.g., 「だれかが/犬に」 = 「犬に/だれかが」)

Enc-Dec with Attention [Bahdanau+ 15] - encodes / decodes “word-by-word”

Two step decoding - First a chunk, then words inside the chunk

Two additional connections 1. to capture the interaction between chunks 2. to memorize peprevious outputs well

!"#$%&

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3#,%2(#$2(#*$')#*%!+'1$+($+&)'*!%1!$6'*$!"#$+0#%$(2))'*! +1$&%!#*+%.$0#5#.')&#1!$9$%1$+1!#*6%,#$6'*$%$(23(!%1,#$')#*%!+'1$%!$%!'&+,$.#5#.$/%($0#5#.')#0$8$

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<.=+&'()*+ 457> 7;541

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1. Some languages use many words to represent one thing while others use less words

2. Some languages are free word-order while others are not

decodes sentences in a “chunk-by-chunk” manner toovercome the differencesof length and word-order

Sequence of a sentence becomes much shorter Fixed word order and free chunk order can be modeled independenlty

か が 犬 に

だれかが

犬に

end-of-chunk

!"#$%&'()('*+,,

-"./0(/*1(2#($3(14

だれ

5./6&'()('*+,,

#7680(

9.6('1*:;<(6*=./6*./6(/*8$6*:/((*=./6*./6(/*;$6(7($6($0'>4

9.6('*?

か 犬 にend-of-chunkが

!"#$%&'()('*+,,

だれ

-./0&'()('*+,,

1.0('*23*4$5(/&6"#$%*6.$$(657.$

1.0('*83*-./0&5.&6"#$%*9((0:;6%