Sandhya Singh, Anoop Kunchuku2an, Pushpak Bha2acharyya{sandhya, anoopk, pb}@cse.iitb.ac.in
Center for Indian Language TechnologyIIT Bombay

! At CFILT, English-Indonesian language pairis being experimented as a part of aProject.
! RelaKvely new language pair among Asianlanguage TranslaKons.

! Script is LaKn for both English and Indonesian! Sentence structure followed is SVO (Subject VerbObject)! Not much structural divergence between English andIndonesian! Indonesian is highly aggluKnaKve andmorphologically rich as compared to English
language.
! Indonesian is considered as resource poor language

Experimental Setup (1/2)
PBSMT system(Moses baseline )• MGIZA++ for wordalignment• grow-diag-ﬁnal-end
heurisKc• Lexicalized Reordering

OSM system• Integrates 5-gram based
reordering andtranslaKon in a single
generaKve process(Durrani et al., 2013)• Deals with words along• 5-gram LM with Kneserwith context of source &Ney smoothing using
targetSRILMExperimental Setup (2/2)
Integra7ng NPLM to Integra7ng NNJM toPBSMT system
PBSMT system• Neural Language model withdefault NPLM se]ngs(Vaswani et al., 2013 )• Neural network joint LMusing parallel data
(Devlin et al., 2014)• Word embedding size as 700, • 5-gram LM with 9 source
750, 800 for 5 epochs
context word
• One hidden layer• Integrated as a feature in
PBSMT system• One hidden layer• Integrated as a feature in
PBSMT system

Approach Used

BLEU score

Phrase based SMT

21.74

OperaKon Sequence Model

21.70

0.806182

22.12

0.804933

0.552821.64

0.806033

0.555

22.08

0.806697

0.551880.808943

0.55597Neural LM with OE = 700
Neural LM with OE =750
Neural LM with OE = 800
Joint neural LM*

22.35* WAT submission , OE : Output Embedding

RIBES score

AMFM score0.804986

0.55095
0.552480

Approach Used

BLEU score

Phrase based SMT

22.03

OperaKon Sequence Model*

22.24

Neural LM with OE= 700

22.58

Neural LM with OE = 750

21.99

Neural LM with OE = 800

22.15

Joint Neural LM

22.05*WAT submission, OE: Output Embedding

RIBES score

AMFM score0.78032

0.564580

0.781430

0.566950

0.781983

0.569330

0.780901

0.563400.782302

0.566470

0.781268

0.565860

Reference Sentence

Translated SentenceError Analysis

Moreover, syariah banking has In addiKon, the banking industry
yet to become a naKonal had not so naKonal agenda, said
agenda, Riawan said.
Riawan who also director of the Phrase inserKon
main BMI.
Of course, we will adhere to W e w i l l c e r t a i n l y p a t u h i A l l w o r d s
the rules, Bimo said.
regulaKons, Bimo said.
translated
The Indonesian government The government has cancel
last year canceled 11 foreign- foreign loans from various
funded projects across the creditors to 11 projects in 2006
Phrase dropped
country for various reasons, because various reasons.
the Finance Ministry said.

not! Due to structural similarity, translaKon outputs
are adequate to understand.
! IntegraKng Neural ProbabilisKc LM (NPLM) withaddiKonal data as a feature in PBSMT system
improves the translaKon quality.
! IntegraKng Neural Network Joint Model(Bilingual LM) trained on parallel data as afeature in PBSMT system improves translaKonquality.! Experiment with pure neural machinetranslaKon system
References
1.
2.
3.

Devlin, Jacob, Rabih Zbib, Zhongqiang Huang, Thomas Lamar, Richard M. Schwartz, and John
Makhoul. 2014. "Fast and Robust Neural Network Joint Models for StaKsKcal MachineTranslaKon." In conference of the AssociaKon of ComputaKonal LinguisKcs.
Durrani, Nadir, Alexander M. Fraser, and Helmut Schmid. 2013. "Model With Minimal
TranslaKon Units, But Decode With Phrases." HLT-NAACL
Vaswani, Ashish, Yinggong Zhao, Victoria Fossum, and David Chiang. 2013. "Decoding withLarge-Scale Neural Language Models Improves TranslaKon." In EMNLP.