Mobile and Pervasive Computing - 7 Natural Language Processing

1. Mobile and Pervasive Computing - 7Natural Language Processing Presented by: Dr. Adeel Akram University of Engineering and Technology, Taxila,Pakistan http://web.uettaxila.edu.pk/CMS/AUT2016/teMPCms

2. Outline • Natural Language Processing • Human Computer Dialog Systems • Problems and Success in HCD • Machine Translation • Example based Machine Translation • Projects

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

4. What is NLP? (cont.) • NLP plays a big part in Machine learning techniques: • automating the construction and adaptation of machine dictionaries • modeling human agents • essential component of NLP • closer to AI • We will focus on two main types of NLP: • Human-Computer Dialogue Systems • Machine Translation

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

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

7. The Future of H-C Dialogue Systems • The final end result of human computer dialogue systems: • 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

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

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

10. Problems of Human-Computer Dialogue • Computers need to be able to learn and process colloquial speech • Needed to understand informal speakers: • Understanding varied responses for call systems • Accounting for variations in spoken numbers • Processing colloquialisms is also necessary for seamless dialogue, where the computer must avoid sounding too formal • 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.' "

11. 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. • Electronic calling systems: company-specific • 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. • Physics tutoring system (ITSPOKE) which can analyze and explain errors in the response to a physics problem. • Allows for more complex input than "Yes," "No," or "Flight UA-93" • These still cannot compare to true human-human dialogue.

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

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

14. Example Based Translation • Converts "parallel" lines of text between language • Only accurate for simple lines • Analogy based

15. Future of Machine Translation • Goal: • 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-3PO • Interpreter • Could hear and translate alien languages • Final goal of machine translation • Star Trek: Universal Translator • 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:  • spontaneous conversations • in person • on the telephone • 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.

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, Apple, Google, Autodesk, Symantec, and Intel use it. • Makes customers happy • Still readable though slightly chunkier than human translations

21. Assignment # 6 • Give Presentation on any one of the following projects • Apple Sri • Google Now • Microsoft Cortana

23. Questions???