4. Kebena-English Machine Translation(Msc Thesis)

92 Pages·2016·2.41 MB·English

ARBA MINCH UNIVERSITY
SCHOOL OF GRADUATE STUDIES
INSTITUTE OF TECHNOLOGY

MODELING KEBENA-ENGLISH CORPUS BASED BILINGUAL DICTIONARY:
USING STATISTICAL MACHINE TRANSLATION

A Thesis Submitted to the School of Graduate Studies of Arba Minch University in Partial
Fulfillment of the Requirements for the Degree of Master of Science in Computer Science

BY

GRAGN KEDIR NASSIR

September 2014
A
cknowledgments
This study has been one of the most significant academic challenges I have ever had to face. Without
the support, patience and guidance of the following people and organization, this study would not have
been completed. It is to them that I owe my deepest gratitude.

I would like to thank Dr. Sebsibie H/Mariam , my supervisor, for giving me constructive comments on
every part of the research besides his many other academic and professional commitments. His
wisdom, knowledge and commitment to the highest standards inspired and motivated me. I would like
to express my sincere gratitude to Sisay Adugna for the continuous support of this study, for his
patience, motivation, enthusiasm, and immense knowledge. His guidance helped me in all the time of
research and writing of this thesis.

A big thanks to all the staff of Arba Minch university computer Science and Information Technology
Dep't Specially Prof. Dp Sharma, Dr. Hussien Seid, Mohammed Abebe, Nebiyat Fikru, Addis Hailu
and Mesay Samuel for their wisdom, friendship, advice and assistance during the course of study in
Arba Minch university.

I have to thank the people from Qebena woreda who generously helped me in data collection and
offered me financial support: Kebena Development Association (Hayatu Mude , Muzemil Sied, Bircha
Mifta), Welkite FM (Musema Bedewi), Qebena Woreda Administration ( Mifta Jemal , Mudesir
Hussein and Fetu Muzemil ) really; you all are optimist and I thank you in advance.

I would like to thank my Mam and Dad for giving birth to me at the first place and supporting me
spiritually throughout my life. My Sister Rahmi Yehutmu . and Brother Jemal Kedir. without them this
work would not have been finished. They have been an inspiration throughout my life. They always
supported my dreams and aspirations, and if I do say so myself, I think they did a fine job raising me.
I'd like to thank them for all they have done to me.

The last, but far from least, I would like to thank my all friends specially Kemal Sultan, Redaei
G/Wahid, Kibru G/Michael, Abebe Mideksa and Getachew Gebru. I never ever forget those bad and
good times we had, sleepless night we experienced and the jokes we encompass. You picked me up and
remained me in high spirits throughout the study.
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Table of Contents
Acknowledgments ...................................................................................................................................... i
List of Tables ........................................................................................................................................... vi
List of Figures ......................................................................................................................................... vii
List of Abbreviations ............................................................................................................................. viii
Abstract .................................................................................................................................................... ix
CHAPTER ONE ......................................................................................................................................... 1
INTRODUCTION ....................................................................................................................................... 1
1.1. Introduction ....................................................................................................................................... 1
1.2. Background ....................................................................................................................................... 1
1.3. Statement of the problem and Justification ....................................................................................... 2
1.4. Objectives of the Study ..................................................................................................................... 3
1.4.1. General Objective ...................................................................................................................... 3
1.4.2. Specific Objectives .................................................................................................................... 3
1.5. Significance of the study ................................................................................................................... 4
1.6. Scope and Limitation of the Study .................................................................................................... 4
1.7. Methodology of the study ................................................................................................................. 4
1.7.1. Data preparation and source of data ........................................................................................... 5
1.7.2. Preprocessing the parallel corpus ............................................................................................. 5
1.7.3. Alignment Methodology ............................................................................................................ 6
1.7.4. Training translation model ......................................................................................................... 6
1.7.5. Tuning Translation Model ......................................................................................................... 7
1.7.6. Testing Translation Model ......................................................................................................... 7
1.7.7. Software Tools and Resources ................................................................................................... 7
1.8. Organization of the Thesis ................................................................................................................ 9
CHAPTER TWO ...................................................................................................................................... 11
LITERATURE REVIEW ........................................................................................................................ 11
2.1. Introduction ..................................................................................................................................... 11
2.2. A Brief Overview of Qebena .......................................................................................................... 11
2.2.1. Phonemes of Qebena language (Latin) .................................................................................... 12
2.2.2. Qebena Sentence Structure ...................................................................................................... 16
2.2.4. Punctuation Marks ................................................................................................................... 16
2.2.5. Conjunctions ............................................................................................................................ 17
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2.3. Overview of Bilingual dictionary and Parallel corpora .................................................................. 17
2.3.1. Bilingual dictionary.................................................................................................................. 17
2.3.2. Linguistic corpora .................................................................................................................... 18
2.3.3. Parallel corpora ........................................................................................................................ 19
2.3.4. Applications of parallel corpora ............................................................................................... 20
2.4 . Overview of machine translation ................................................................................................... 21
2.4.1. Rule based Approach ............................................................................................................... 21
2.4.1.1. Transfer-based machine translation ...................................................................................... 22
2.4.1.2. Interlingua machine translation ............................................................................................. 22
2.4.1.3 Dictionary-based machine translation ..................................................................................... 23
2.4.2. Example-based machine translation (EBMT) ........................................................................... 23
2.4.3. Statistical machine translation ................................................................................................. 24
2.4.3.1. Word-based translation ......................................................................................................... 26
2.4.3.2. Phrase-based translation ........................................................................................................ 26
2.4.3.3. Syntax-based translation ....................................................................................................... 26
2.4.3.4. Hierarchical phrase-based translation ................................................................................... 27
2.5. Translation Model ........................................................................................................................... 27
2.6. Basics of Probability ...................................................................................................................... 27
2.7. Related Works ................................................................................................................................. 28
2.7.1. The Preliminary Experiments On English-Amharic Statistical Machine Translation .............. 28
2.7.2. English –Oromo Machine Translation: An Experiment Using a Statistical Approach ............ 29
2.7.3. Dictionary-based Amharic-English Information Retrieval ...................................................... 31
2.7.4. Amharic-English Cross-lingual Information Retrieval: A Corpus Based Approach ........... 32
2.7.5. Cross-Lingual Information Retrieval System for Indian Languages ....................................... 33
2.7.6. Oromo-English Cross Language Information Retrieval .......................................................... 34
CHAPTER THREE .................................................................................................................................. 37
MODELING QEBENA-ENGLISH CORPUS BASED BILINGUAL DICTIONARY ...................... 37
3.1. Introduction ..................................................................................................................................... 37
3.2. Architecture of the model ............................................................................................................... 38
3.3. Data Source and Data Collection .................................................................................................... 39
3.4. Data Preprocessing .......................................................................................................................... 40
3.5. Parallel Corpus Preparation ............................................................................................................ 41
3.6. Parallel Corpus Preprocessing ........................................................................................................ 42
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3.6.1. Tokenization ............................................................................................................................ 42
3.6.2. True casing and Lower casing ................................................................................................. 43
3.6.3. Cleaning ................................................................................................................................... 43
3.7. Language Model ............................................................................................................................. 43
3.7.1. Markov Chain rule ................................................................................................................... 44
3.8. Word Alignments ............................................................................................................................ 45
3.8.1. Vocabulary File ........................................................................................................................ 45
3.8.2 Bitext File .................................................................................................................................. 46
3.8.3. Word Alignment Creation ........................................................................................................ 46
3.9. Translation Modeling ...................................................................................................................... 48
3.9.1 Decoding ................................................................................................................................... 50
3.10. Model Tuning ................................................................................................................................ 51
3.11. Model Evaluation .......................................................................................................................... 51
3.11.1. Blue Metrics ........................................................................................................................... 52
CHAPTER FOUR ..................................................................................................................................... 53
EXPERIMENTATION AND ANALYSIS .............................................................................................. 53
4.1. Introduction ..................................................................................................................................... 53
4.2. Working Environment .................................................................................................................... 53
4.3. Experimentation .............................................................................................................................. 53
4.3.1. Phase 1: Experiment on parallel corpus ................................................................................... 53
4.3.1.1. Corpus Tokenization ............................................................................................................. 54
4.3.1.2. Corpus Truecasing ................................................................................................................. 55
4.3.1.3. Corpus Cleaning ..................................................................................................................... 56
4.3.2. Phase 2: Experiment on language modeling. ......................................................................... 57
4.3.3. Phase 3: Experiment on training the translation model............................................................ 59
4.3.3.1. Phrase table (alignment table) ............................................................................................... 60
4.3.3.2. Lexicalized reordering table .................................................................................................. 60
4.3.3.3. Moses Configuration File ...................................................................................................... 61
4.3.4. Phase 4: Experiment on tuning the translation system. ............................................................ 62
4.4. Analysis of translation system ........................................................................................................ 63
4.4.1. Testing ...................................................................................................................................... 63
4.4.2. Results ...................................................................................................................................... 64
4.4.2.1. Blue Score Before Tuning ..................................................................................................... 65
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4.4.2.2. Blue Score After Tuning ....................................................................................................... 65
CHAPTER FIVE ...................................................................................................................................... 68
CONCLUSIONS AND RECOMMENDATIONS .................................................................................. 68
5.1. Introduction ..................................................................................................................................... 68
5.2. Conclusion ...................................................................................................................................... 68
5.3. Recommendations ........................................................................................................................... 70
REFERENCES .................................................................................................................................. 71
Appendices .............................................................................................................................................. 76
Appendix A ......................................................................................................................................... 76
Appendix B ......................................................................................................................................... 77
Appendix C ......................................................................................................................................... 80

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List of Tables
Table 2.1. Consonant phonemes of Qebena .............................................................................................. 13
Table 2.2. Description of consonant phonemes of Qebena ....................................................................... 14
Table 2.3. Six vowels of Qebena Alphabet ............................................................................................... 15
Table 2.4. Description of the vowel phonemes of Qebena ........................................................................ 15
Table 2.5. Some Qebena conjunction ........................................................................................................ 17
Table 2.6. Example of bilingual corpus .................................................................................................... 23
Table 2.7. Summary of average results for the three runs ......................................................................... 36
Table 3.1. Sample Qebena -English preprocessed parallel text ................................................................ 40
Table 3.2. Summary of parallel data used for modeling Q-E bilingual dictionary. .................................. 42
Table 4.1. Summary of Q-E bilingual dictionary BLUE score ................................................................. 66
Table 5.1. Size of parallel data .................................................................................................................. 69
Table 5.1. Obtained BLUE score .............................................................................................................. 69

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List of Figures
Figure 2.1. Different methods of machine translation .............................................................................. 22
Figure 3.1. General architecture of Qebena-English bilingual dictionary ................................................ 38
Figure 3.2. Sample source parallel data from grammar of Qebena ........................................................... 39
Figure 3.3. Sample Qebena-English parallel corpus ................................................................................. 41
Figure 3.4. Alignment between Qebena and English with the same word order ...................................... 46
Figure 3.5. Alignment between Qebena and English with the different word order ................................ 47
Figure 3.6. One Qebena word requires two English words to capture the same meaning ........................ 47
Figure 3.7. The English function word had does not have a clear equivalent in Qebena ......................... 48
Figure 3.8. Decoding process in machine translation ............................................................................... 50
Figure 4.1. Sample tokenized parallel corpus ........................................................................................... 55
Figure 4.2. Sample truecased parallel corpus ............................................................................................ 56
Figure 4.3. Sample cleaned parallel corpus .............................................................................................. 57
Figure 4.4. Sample smoothed and boundary symbol added model ........................................................... 58
Figure 4.5. Sample 3-gram language model ............................................................................................. 59
Figure 4.6. Phrase table (word alignment) ................................................................................................ 60
Figure 4.7. Lexicalized reordering table ................................................................................................... 61
Figure 4.8. Moses Configuration File ....................................................................................................... 62
Figure 4.9. Translated English testing file ................................................................................................ 64
Figure 4.10. Blue score result before tuning ............................................................................................. 65
Figure 4.11. Blue score result after tuning ................................................................................................ 66

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List of Abbreviations
ACL Association for Computational Linguistics
BLEU Bilingual Evaluation Understudy
BLIR Bilingual Information Retrieval
CLIR Cross Language Information Retrieval
DOP Data Oriented Parsing
EBMT Example-based Machine Translation
EFL English as Foreign Language
EM Estimate Maximization
HMM Hidden Markov Model
IBM International Business Machines
IR Information Retrieval
IRSTLM Institution of research and Scientific Technology
KDA Kebena Development Association
LGPL Lesser General Public License
LM Language model
LTS Long Term Support
MERT Minimum error rate training
MT Machine Translation
MTTK Machine Translation Toolkit
OOV Out of Vocabulary
POS Part of Speech
QFP Qebena File Processor
Q-E Qebena to English
RBMT Rule based Machine translation
SMT Statistical Machine Translation
SNNPR South Nation, Nationality and People Region
SOV Subject-Object-Verb
SVO Subject-Verb-Object
TM Translation Modeling
UTF Unicode Transformation Format
WWW World Wide Web
WMT Workshop on Machine Translation

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Abstract
In the world we live in today, understanding language and its usage is very important. Dictionary has been
used for many years to translate texts or just to look up single words. It has become an essential part of our
modern life and is playing a vital role to increase communication among people. Many dictionaries are
available for nearly every combination of popular languages. They also often exist between language pairs
where one language is popular and the other is not, Bilingual dictionaries for uncommon languages are much
less likely to exist. Hence, the need to develop Bilingual dictionaries for uncommon languages like that of
Qebena becomes more noticeable.

Therefore, the aim of this study was modeling Qebena-English corpus based bilingual dictionary and
analyzing the output of the system with the objective of identifying the challenges that need to be tackled. The
development approach used in this study was corpus based statistical machine translation(SMT), which
allows training of translation model based on parallel corpora. Parallel corpora plays a fundamental role to
model bilingual dictionaries in SMT system. Today, there are quite a large number of parallel corpora
available for most of major languages; however, there does not seem to be as many available for the
Qebena-English language pair. Consequently, looking for parallel corpora for Qebena-English language pair
was the challenging task in this study. The only available parallel documents found for these languages pair
were Grammar of Qebena from council of South Nation Nationality People (SNNP) and some parts of Grade
4 Qebena student's text book from elementary school of Qebena woreda. The resulted parallel corpora of
5,144 sentences having 15,951 words was used for training, tuning and testing the translation system.

In order to measure the accuracy of the translation system, the experiment has been conducted on
parallel Qebena-English data that are different from training data with 240 Qebena-English sentences
having 1,550 words. Accordingly, the BLEU score result was 32.49 %. A 6 % increase in BLEU score was
achieved by tuning the translation system. Due to the scarcity of resources for Qebena language, the
researcher could not get as many parallel documents as needed for the experiment. However, using a limited
corpus, the translation accuracy we achieved was not too low as compared to the systems built on a relatively
sufficient amount of resource. Therefore, based on the result we obtained from the model, with the use of
large parallel Qebena-English document collections, it is possible to develop bilingual dictionary using
statistical machine translation approach for Qebena-English language pairs.

Keywords:- Communication, Bilingual dictionary, Parallel Corpora, Statistical Machine Translation
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