This slides covers introduction about machine translation, some technique using in MT such as example based MT and statistical MT, main challenge facing us in machine translation, and some examples of application using in MT

1. Machine Translation
Mohamed Hassan

2.  Introduction
 Main challenges
 Techniques
 Features

3. Introduction
 Machine translation:-
 Machine Translation has been defined as the process that
utilizes computer software to translate text from one
natural language(such as English) to another (such as
Arabic).
 The idea of machine translation may be traced back to
the 17th century
 MT on the web starts with Systran offering free translation
of small texts (1996)

4. Technique
 Example-based MT
 Dictionary-based
 Rule-based
 Hybrid MT
 Neural MT
 Statistical
 Interlingual
 Transfer-based

5. Example-based MT
 characterized by its use of a bilingual corpus with parallel
texts as its main knowledge base.
 It is essentially a translation by analogy
 Ex
 English How much is that umbrella
 Arabic ‫المظله‬ ‫هذه‬ ‫سعر‬ ‫كم‬
 English How much is that doggie
 Arabic ‫الكلب‬ ‫هذا‬ ‫سعر‬ ‫كم‬

6. Dictionary-based
 The words will be translated as a dictionary does — word
by word, usually without much correlation of meaning
between them

7. Rule-based
 RBMT involves more information about the linguistics of the
source and target languages ,using the syntactic rules and
semantic analysis of both languages
This type of translation is used mostly in the creation
of dictionaries and grammar programs

8. Interlingual
 instance of rule-based machine-translation
 Itis necessary to have an intermediate representation(interlingua)
that captures the "meaning" of the original sentence in order to
generate the correct translation
 "language neutral" representation that is independent of any language
 Advantage: one of the major advantages of this system is that the
interlingua becomes more valuable as the number of target languages
it can be turned into increases
 the only interlingual machine translation system that has been made
operational at the commercial level is the KANT system

9. Transfer-based
 Itis necessary to have an intermediate representation that
captures the "meaning" of the original sentence in order
to generate the correct translation
 it depends partially on the language pair involved in the
translation

10. Statistical
 using statistical methods based on bilingual text corpora,
such as the Canadian Hansard corpus
 The idea behind statistical machine translation comes
from information theory

11. Hybrid MT
combination of statistical and rule-
based translation methodologies

12. Neural MT
 neural network is trained by deep
learning techniques

13. Challenges in MT
 Ambiguity
Ex1:
Book the flight -> verb
Read the book -> noun
Ex2:
Kill a man (‫)قتل‬
Kill a process (‫انهاء‬)
Ex3:
she couldn’t bear children
‫تستطيع‬ ‫ال‬‫تحمل‬‫االطفال‬
‫تستطيع‬ ‫ال‬‫انجاب‬‫اطفال‬

14. Challenges in MT
Different word orders
English word order : subject –verb –object
Mohamed is at home
Arabic word order:
‫المنزل‬ ‫في‬ ‫يتواجد‬ ‫محمد‬(‫اسميه‬ ‫جمله‬)
‫المنزل‬ ‫في‬ ‫محمد‬ ‫يتواجد‬(‫فعليه‬ ‫جمله‬)
Japanese: subject –object- verb

15. Challenges in MT
 Compound Words
Arabic ‫ا‬َ‫ه‬‫و‬ُ‫م‬ُ‫ك‬ُ‫م‬ِ‫ز‬ْ‫ل‬ُ‫ن‬َ‫أ‬
English Shall we compel you to accept it
 Missing Names
A language may not have a word for a certain
action or object that exists in another language
ksnona (Greek)
guest room(english)

16. Application
Google translator
Bing Translator
SYSTRAN
Asia Online