Natural Language Processing (NLP) is an interdisciplinary field that combines computational methods to analyze human language and develop applications for intelligent language processing. The document discusses two main types of NLP: human-computer dialogue systems, which aim for seamless interaction and understanding of informal speech, and machine translation, which facilitates communication across languages but struggles with ambiguity and contextual meanings. Advances in NLP are aimed at improving the accuracy of translations and the effectiveness of dialogue systems, exemplified by both fictional and real-world applications.

1. Natural
Language
Processing
PRESENT BY : RONAK R. DEVANI

2. What is Natural Language Processing?
• NLP is an interdisciplinary field that uses computational methods
to:
o Investigate the properties of written human language and
model the cognitive mechanisms underlying the understanding
and production of written language.
o Develop novel practical applications involving the intelligent
processing of written human language by computer.

3. What is NLP? (cont.)
• NLP plays a big part in Machine learning techniques:
o Automating the construction and adaptation of machine
dictionaries
o Modeling human agents' desires and beliefs
 Essential component of NLP
 Closer to AI
• We will focus on two main types of NLP:
o Human-Computer Dialogue Systems
o Machine Translation

4. Human-Computer Dialogue Systems
• Usually with the computer modelling a human dialogue participant
• Will be able:
o To converse in similar linguistic style
o Discuss the topic
o Hopefully teach

5. Current Capabilities of Dialogue
Systems
• Simple voice communication with machines
o Personal computers
o Interactive answering machines
o Voice dialing of mobile telephones
o Vehicle systems
o Can access online as well as stored information
• Currently working to improve

6. The Future of H-C Dialogue Systems
• The final end result of human computer dialogue systems:
o Seamless spoken interaction between a computer and a
human
• This would be a major component of making an AI that can pass
the Turing Test
• Be able to have a computer function as a teacher

7. Human Computer Dialogue in Fiction
• Halo's Cortana AI
o Made from models of a real human brain
o Made to run the ship
o Made very human conversations
• Ender's Game series: Jane
o Made from “Philotic Connection"
o Human conversation

8. Problems of Human-Computer
Dialogue
• At the moment, most common computer dialogue systems (call
systems, chatter bots, etc.) cannot handle arbitrary input
o In many cases, the computer can only respond to "expected"
speech
o Call systems often compensate with "Sorry, I didn't get that,"
when something unexpected is said.

9. Problems of Human-Computer
Dialogue
• Computers need to be able to learn and process colloquial speech
• Needed to understand informal speakers:
o Understanding varied responses for call systems
o Accounting for variations in spoken numbers
• Processing colloquialisms is also necessary for seamless dialogue,
where the computer must avoid sounding too formal
o John Connor: "No, no, no, no. You gotta listen to the way people
talk. You don't say 'affirmative,' or [stuff] like that. You say 'no
problemo.' "

10. Successes of Human-Computer
Dialogue
• So far, human-computer dialogue has been most successful in
applications where information about a specific topic is sought
from the computer.
o Electronic calling systems: company-specific
o Travel agents: specific to an airline or destination
• However, more complex systems of human-computer dialogue
have been produced which can interpret more varied input.
o Physics tutoring system (ITSPOKE) which can analyze and
explain errors in the response to a physics problem.
o Allows for more complex input than "Yes," "No," or "Flight UA-
93"
• These still cannot compare to true human-human dialogue.

11. Machine Translation
• Important for:
o accessing information in a foreign language
o communication with speakers of other languages
• The majority of documents on the world wide web are in
languages other than English

12. Statistical Translation
• Rule based
• Works relatively well with large sets of data
• Used probability to translate text
• Natural translations
• Google

13. Example Based Translation
• Converts "parallel" lines of text between language
• Only accurate for simple lines
• Minimal pairs are easy
• Analogy based

14. Paraphrasing
• Takes words and makes them simpler automatically
• For example in Spanish conjugated words like usado may be
changed to usar

15. Future of Machine Translation
• Goal:
o Aim to be able to flawlessly translate languages
• Link Human-Computer Dialogue and Machine Translation
• Have someone be able to talk in one language to a computer,
translate for another person
• Translated Video Chat

16. Machine Translation in Fiction
• Star Wars: C-3P0
o Interpreter
o Could hear and translate alien languages
o Final goal of machine translation
• Star Trek: Universal Translator
o Computer can seamlessly translate alien languages

17. Problems
• Works well only with predictable texts.
• Doesn't work well with domains where people want translation
the most:
o spontaneous conversations
o in person
o on the telephone
o and on the Internet.

18. Problems
• Computers can't deal with ambiguity, syntactic irregularity,
multiple word meanings and the influence of context.
Time flies like an arrow.
Fruit flies like a banana.
• Accurate translation requires an understanding of the text,
situation, and a lot of facts about the world in general.
The box is in the pen.

19. Problems
• The sign is describing a
restaurant (the Chinese
text, 餐厅, means "dining
hall").
• In the process of making
the sign, the producers
tried to translate Chinese
text into English with a
machine translation
system, but the
software didn't work,
producing the error
message,
"Translation Server Error."
• The software's user didn't
know English and thought
the error message was the
translation.

20. Successes
• Product knowledge bases need to be translated into multiple
languages
• Hiring a large multilingual support staff is expensive
• Machine translation is cheaper and accurate with predictable
texts.
• Microsoft, Autodesk, Symantec, and Intel use it.
o Makes customers happy
o Still readable though slightly chunkier than human
translations

21. THANK YOU