NAIST at WAT 2014

Forest-to-String SMT for Asian
Language Translation:
NAIST at WAT 2014

Graham Neubig
Nara Institute of Science and Technology (NAIST)
2014-10-4

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NAIST at WAT 2014

Features of ASPEC●Translation between languages with different
grammatical structures

流動

プラズマを正確に測定するために画像を再構成した。

an image was reconstituted in order to measure flowing plasma correctly .

●We all know: Phrase-based MT is not enoughfor the accurate measurement of plasma flow image was reconstructed .

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NAIST at WAT 2014

Solution?: 2-step Translation Process●Pre-ordering [Weblio, SAS_MT, NII, TMU, NICT]

我々 は 科学 論文
を 翻訳 する●

我々 翻訳 する
科学 論文

we translatescientific papers

RBMT+Statistical Post Editing [TOSHIBA, EIWA]

我々 は 科学 論文
を 翻訳 する

we translatescience thesis

we translatescientific papers

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This is a lot of work... :(How do I make goodJapanese-English
preordering rules?!
How do I make goodJapanese-Chinese
preorderering rules?!

What about error propagation?

What if better preordering
accuracy doesn't equal bettertranslation accuracy?
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NAIST at WAT 2014Evidence

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Our Solution: Tree-to-String Translation[Liu+ 06]x1 with x0

VP0-5
VP2-5

PP0-1
N0
友達

PP2-3

P1とx1 x0
VP4-5

N2P3

V4 SUF5

ご飯を食べ
x1x1た

atea meal
my friendx0x0

ate a meal with my friend
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Requirements for aTree-to-String Model
Source Sentence
ParserParallel
Corpus

これ は テスト です 。

データ を 使用 します 。

This is a test .

It uses data .

Rule Extraction
Rule Scoring
Optimization

Alignments
Tree-to-StringModel7

NAIST at WAT 2014

Reducing our work load.

XHow do I make goodJapanese-English
preordering rules?!

XHow do I make goodJapanese-Chinese
preorderering rules?!

What about error propagation?

X

What if better preordering
accuracy doesn't equal bettertranslation accuracy?
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Forest-to-string Translation[Mi+ 08]
S
0,7
VP
1,7

NP
2,7
PP
4,7

NP
0,1
PRP VBD0,1 1,2I saw

NP
5,7

NP
2,4
DT
2,3

NN
3,4

IN
4,5

DT
5,6

NN
6,7

a girl with a telescope
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NAIST at WAT 2014

Travatar Toolkit●

Forest-to-string translation toolkit●Supports training, decoding●Includes preprocessing scripts for parsing, etc.

●

Many other features (optimization, Hiero, etc...)Available open source!
http://phontron.com/travatar

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NAIST at WAT 2014

NAIST WAT System

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WAT Results
First place in all tasks!
BLEU
50
40+13.0+3.6+2.2+15.0
40+3.820

OtherNAIST

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100+28.3

60+1.8+2.7

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HUMAN

en-ja

ja-enzh-ja

ja-zh

0en-ja ja-en zh-ja ja-zh

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System Elements

Travatar!
Same as [Neubig & Duh, ACL2014]

Recurrent Neural
Net Language Model

Pre/post Processing(UNK splitting, transliteration)
Dictionaries

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NAIST at WAT 2014

Recurrent Neural Network LMIcaneat

anapple 


●Vector representation → robustness●

Recurrent architecture → longer context
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Pre/post processing
UNK segmentation (ja-en)

Kanji Normalization (ja-zh, zh-ja)

試験 管立てイチョウ黄叶臭気鉴定师球 内部 試験 管 立て

イチョウ黄葉

臭気鑑定師

Transliteration (ja-en)

Dictionary addition (ja-en)

球内部Japan インテック

膿瘍典型Japan Intekku

apostemaarchetype15

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Conclusion

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Future WorkLOSE at next year's WAT.
(Make Travatar so easy to use that others
can use it to make really good MT systems
for Asian languages.)

Starting soon! Training scripts to be available:
http://phontron.com/project/wat201417