Leveraging ASEAN Economic Community
through Language Translation ServicesHammam Riza
Center for Information and Communication Technology
Agency for the Assessment and Application of Technology(BPPT)Abstract
In this talk, I will present the activities of BPPT’s Speech and Translation Research Group(PERISALAH), including the latest bidirectional Indonesian-English statistical machine translation,and our contribution toward developing network-based ASEAN Languages Translation PublicServices (ASEAN-MT). This collaboration work involving 7 ASEAN languages: Thai, Malay,Indonesian, Lao, Cambodian, Vietnamese, Myanmar and English as predominant language in
ASEAN countries. As part of the inauguration of ASEAN Economic Communities 2015, theASEAN-MT public service in the travel domain will be brought online with acceptable responsetime with at least 3 of 5 score of user satisfaction on the service is achieved.
This joint effort not only building a practical network-based service on ASEAN languages texttranslation in the tourism domain, but most important to strengthen ASEAN collaboration inscience and technology, sharing languages resources and knowledge of the translation
technology among ASEAN member states and other countries. As part of improving IndonesianEnglish statistical machine translation, I will introduce the new 2014 project within Indonesian
Language Development Agency (Badan Pengembangan Bahasa Indonesia) on building large scaleparallel corpora based on ANTARA News corpora and e-KBBI (the official Indonesian Large
Electronic Dictionary)Introduction
 READY: This is ASEAN’s time. In the geographic
heart of the world’s premier growth corridor,
ASEAN is poised to “seize the moment.”
 SET: With a market of over 600 million
consumers and combined GDP of nearly US$3trillion, ASEAN is offering a future of prosperityand stability. The AEC is one of the foundations ofthat future.
 GO: Agreements on trade, services andinvestment are changing the economic landscapeand allowing the freer flow of goods, services andpeople across the region.

AEC

The ASEAN Economic
Community (AEC) shall be thegoal of regional economic
integration by 2015.
AEC envisages the following
key characteristics:(a) a single market andproduction base,(b) a highly competitive
economic region,(c) a region of equitableeconomic development,(d) a region fully integrated intothe global economy.
Total Population: 600 million+
GDP: USD $3 trillion

ASEAN Languages

Network-based ASEAN languagetranslation for public services(ASEAN-MT)• ASEAN-MT Background
ASEAN languages translation is increasiingly
important to support the coming AEC 2015
• The ASEAN-MT Project- Endorsed by SCMIT in May 2011
- Approved for ASF partial support in May 2012
- Start in July 2012
- Launch in Oct 2015

ASEAN-MT Objectives• R&D on network-based ASEAN languages machinetranslation for the public use• Sharing language translation knowledge andresources among ASEAN countries• Reducing the language barrier among and outside
ASEAN after the beginning of ASEAN Community in2015.

ASEAN-MT Approach
Statistical based Machine Translation

The 2nd working Committee Meeting, Dec 19-20, 20139Demonstration at SCMIT and
COST meeting May 23, 2013

11

The 2nd working Committee Meeting, Dec 19-20, 2013

ASEAN-MT Activities
1st year

Activity

2012
Y1.1

Kick-off meeting (WC meeting 1)

Y1.2

Resource development

Y1.3

Technology workshop

Y2.1

Individual MT engine development

Y2.2

Service system development

Y2.3

Client application development

Y2.4Demonstration in ASEAN COST

Y2.5

WC meeting 2

Y3.1

Resource extension

Y3.2

Improvement and evaluation

Y3.3

WC meeting 3 and press

Y3.4

Conclusion and report

2nd year

2013

5512

3rd year

2014

2015

MT Public ServiceThailand developers
developed an application
which will help theAssociation of Southeast
Asian Nation in terms oflanguage barriers.
The app is called an ASEAN
One App. Right now the app
is on its first phase. It willtranslate a local language
into 11 different ASEAN
language which includes
English.

Source: http://philnews.ph/2012/03/09/asean-one-app-plans-speak-11-languages-developed-thailand/

Extending
ASEAN+ICJK
Languages

WAT 2015?

SMT Indonesian-English

Translation Technology
Application Roadmap
Mobile Perisalah(Cellular OTT)Transcription
Summarization
System2013-2014

Public Service,
IndonesianEnglish SMT
Speech Translation
System(IndonesianEnglish)

2015-2016

Network-based ASEAN Language Translation Public Service (ASEAN-MT)
Universal Speech Translation Advance Research (U-STAR)
Speech Corpora, Parallel Text Corpora ,TTS, ASR, NLP Tools
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BPPT - Digital Signal Processing (DSP) Laboratory
Speech Procesing

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Indonesian Language Tools••

Language Processing Tools• Speech– Stemmer, POS Tagger– Named Entity Tagger, Phrase Chunker– Statistical Constituent Parser andDependency Parser– Indonesian Reference Resolution andSemantic Analysis– Indonesian MindMap
Generator: http://mindmap.kataku.org– Game for learning JapaneseIndonesia: http://honyaku.kataku.org– Purchase pattern on social
media: http://elysis.kataku.org•Text Mining•– Indonesian Question Answering System– Open Domain– Closed Domain with ontology– Factoid, List Factoid, Non Factoid– Indonesian Information Extraction•••••Indonesian AutomaticSpeech Recognizer
Indonesian Speech
Synthesizer
Preliminary Research on
Indonesian TTS based on“Unit Selection” approach
Rebuild of Indonesian
Diphone Database for
Diphone Concatenation
based Indonesian TTS
Building Indonesian TTS
based on MARY platform
Improvement ofIndonesian Prosody
Indonesian Syllable TTS
for special purpose
application

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Corpus Development 2014
• Perisalah v.3 Speech Corpora• Perisalah v.3 POS-TaggedCorpus (with Indonesian
Language DevelopmentCenter)• Corpus Management System• Indonesian POS Tagged Corpus• Extended Word Clustering
Algorithm• Indonesian Question Answering
Using of Indonesian• TTS for blind operator who work inCall Center• Indonesian Named Entity TaggedCorpus• Regional Language LexicalDatabase• Indonesian-Japanese Parallel
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Indonesian ASEAN-MT: Improving Statistical
Machine Translation with POS Tagged Corpus
Sujaini et.al, Comparison of POS Tagset for improving English-Indonesian
SMT, O-COCOSDA, 2014Corpus

1. Algorithmic improvement
SMT

Extended WSB vs WSB
vs MKCLS

Word cluster

2. POS TagsetCorpus

Computed POS
vs Grammar POS
vs without POS

SMTWord cluster
Parallel Corpus(tagged)PoS Tagset
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Indonesian ASEAN-MT: Improving Statistical MachineTranslation with POS Tagged Corpus
Experiment on
WSB Algorithm

12.00
10.00
8.00

Improving BLEU score using
WSB Algorithm against mkcls

6.00
4.00
2.00
0.00
wsb

ewsb agg

ewsb div

Base value mkcls = 38,31%

ewsb+morf

Algoritma

Improving SMTwith POS
BLEU score of SMT with
6 test groups

9.00

Peningkatan nilai BLEU (%)

Peningkatan nilai BLEU (%)

14.00

8.00
7.00
6.00
5.00

TataBahasa

4.00

BantuanKomputasi

3.00
2.00
1.00
0.00
A

BC

DE

rerataGrup Uji

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EXTENDED WORD SIMILARITY BASED CLUSTERINGON UNSUPERVISED POS INDUCTION TO IMPROVEENGLISH-INDONESIANSTATISTICAL MACHINE TRANSLATION

State of the Art
Factored based SMT
No

ModelBahasa

Koehn dan Hieu 2007
Hoang

PoS

English–German

0,61

Koehn dan Hieu 2007
Hoang

PoS %
morfologis

English–Spanish

3,59

2

Bojar2007

morfologis

English-Czech

7,75

3

Youssef dkk.

2009

PoS

English–Arabic

4,92

4

Razavian dkk.

2010

Suffix LM

English–Iraqi

5,06Razavian dkk.

2010

Suffix LM

Spanish-English

0,95

Razavian dkk.

2010

Suffix LM

Arabic-English

2,44

Wuebker dkk.

2013

WC

French-German

1,40

Wuebker dkk.

2013

WC

German-English

0,30

15

Peneliti

Tahun

Peningkatannilai BLEU (%)

PoS Tagset for
Bahasa Indonesia
37 tags
29 tags
48 tags

35 tags

19 tags

Objectives








We present the unsupervised Part-of-Speech (PoS)
induction algorithm to improve translations quality on
statistical machine translation.
The proposed algorithm is an extension of the algorithm
Word-Similarity-Based (WSB) clustering.
In the clustering, the similarity between words ismeasured by its grammatical relation with other words.
The grammatical relation is represented as the n-gramrelation.
We extend the WSB clustering by take into account forthe previous words in measuring the grammatical relation.
The clustering results are then used in the EnglishIndonesia statistical machine translation.

Experiments
The experiments were conducted using seven sets of
2,000 English-Indonesian parallel sentences as thetraining data for the machine translation and 300
sentences as the testing data.
The machine translation tools used in this research areMoses as decoding tool, SRILM as language modelprocessor, and GIZA++ as phrase translation modelprocessor. BLEU (Bilingual Evaluation Understudy) scoreis used to evaluate the quality of translation result
There were four clustering algorithms employed in theexperiment: mkcls (word clustering provided in Moses),
WSB by Jeff, EWSB-Agglomerative and EWSB-Divisive.

The BLEU score

Notes :EWSB A = EWSB Agglomerative
EWSB D = EWSB Divisive

Conclusion
Based on Indonesian language characteristics,
we extended the word similarity based clustering
to enhance the quality of English-Indonesianmachine translation. Using 14,000 EnglishIndonesian sentence pair as the training data and300 sentences as the testing data, theexperimental result showed that our extension
gave higher BLEU score compared to the original
WSB clustering, and it increased the translation
accuracy 2.07%.

Harigato Gozaimasu

THANK YOU

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