1. Introduction to Machine Translation
CSC Machine Translation
Fall Dr. Tom Way

2. Types of Machine Translation

3. Types of Machine Translation
rule based
dictionary based - EASIEST - also called word-based or word-for-word approach
transfer rule approach - try to use the meaning of source language to output the same meaning in the target language
interlingual - translate via a language neutral intermediate form
statistical
calculate most likely translation by using pairs of translation (bilingual text corpora)
neural networks - HARDEST - state of the art, really a variation of statistical
example based
use simple examples in one language to generate the same thing in another language

4. History of Machine Translation

5. History of Machine Translation (Based on work by John Hutchins, mt-archive.info)
Before the computer: In the mid 1930s, a French-Armenian Georges Artsrouni and a Russian Petr Troyanskii applied for patents for ‘translating machines’.
The pioneers ( ): the first public MT demo was given in (by IBM and Georgetown University).
Machine translation was one of the first applications envisioned for computers

6. History of MT (2)
Warren Weaver, PhD was an American scientist, mathematician, and science administrator. He is widely recognized as one of the pioneers of machine translation, and as an important figure in creating support for science in the United States.

7. History of MT (3)
First demonstrated by IBM in 1954 with a basic word-for-word translation system

8. History of MT (4) The decade of optimism (1954-1966) ended with the…
ALPAC (Automatic Language Processing Advisory Committee) report in 1966: “There is no immediate or predictable prospect of useful machine translation."

9. History of MT (5) The ALPAC Report
The ALPAC (Automatic Language Processing Advisory Committee) was a govt. committee of seven scientists.
Their 1966 report was very skeptical of the progress in computational linguistics and machine translation.

10. History of MT (6) The aftermath of the ALPAC report…
Research on machine translation virtually stopped from 1966 to 1980

11. History of MT (7) Then, a rebirth…
The 1980s: Interlingua, example-based Machine Translation
The 1990s: Statistical MT
The 2000s: Hybrid MT
The 2010s: Google, real-time, mobile, Crowdsourcing, more hybrid approaches

12. Machine Translation today

13. Where are we now?
Huge potential/need due to the internet, globalization and international politics.
Quick development time due to Statistical Machine Translation (SMT), the availability of parallel data and computers.
Translation is reasonable for language pairs with a large amount of resources.
Start to include more “minor” languages.

14. Rule-based MT
The Vauquois Triangle

15. Statistical MT
The Rosetta Stone

16. What is MT good for? Rough translation: web data
Computer-aided human translation
Translation for limited domain
Cross-lingual IR
Machines beat humans at:
Speed: much faster than humans
Memory: can easily memorize millions of word/phrase translations.
Manpower: machines are much cheaper than humans
Fast learner: it takes minutes or hours to build a new system.
Never complain, never get tired, …

17. Interest in Machine Translation (1)
Commercial interest:
U.S. has invested in machine translation (MT) for intelligence purposes
MT is popular on the web—it is the most used of Google’s special features
EU spends more than $1 billion on translation costs each year.
(Semi-)automated translation could lead to huge savings

18. Interest in Machine Translation (2)
Academic interest:
One of the most challenging problems in NLP research
Requires knowledge from many NLP sub-areas, e.g., lexical semantics, syntactic parsing, morphological analysis, statistical modeling,…
Being able to establish links between two languages allows for transferring resources from one language to another

19. Goals & Uses Translating Summarizing Communicating Pre-editing
Grammar analysis
Analyzing text
Understanding text and images

20. Do we really need Machine Translation?

21. Languages on the Internet

22. Languages on Twitter

23. Languages in Los Angeles

24. Why do we need MT?

25. Why do we need MT?

26. Why do we need MT?

27. Why is MT hard?

28. Why is MT hard?

29. Why is MT hard?

30. Why is MT hard? For example…
Commercial system “Language Weaver” created in 2002
Uses statistical techniques from cryptography and machine to acquire statistical models from human translations
Sold in 2010 for $42.5 million

31. “Language Weaver” SMT System – Comparison: Arabic to English
v February 2005
v.2.0 – October 2003
v.2.4 – October 2004