ai-cl_introartificial intelligence: an easy introduction from a computational linguistic perspective

8/9/2019 ai-cl_introArtificial Intelligence: An easy introduction from a computational linguistic perspective

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Artificial Intelligence (AI) Computational Linguistics (CL) The TALK project Learning software project

Artificial Intelligence:

An easy introduction from a

computational linguistic perspective

Ciprian-Virgil Gerstenberger

Saami Language Technology, Giellatekno, University of Troms, Norway

24.11.2011 University of Murmansk
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Outline

Artificial Intelligence (AI)
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Outline


Computational Linguistics (CL)

The TALK project
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Outline


Computational Linguistics (CL)

The TALK project

Learning software project
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What is Artificial Intelligence?

What is artificial?

all human made

What is intelligence?

the capacity to learn and solve problems(Webster dictionary)

the ability to think and act rationally

C C
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Definitions

Artificial Intelligence is a branch of Science which deals with helpingmachines find solutions to complex problems in a more human-likefashion. This generally involves borrowing characteristics from humanintelligence, and applying them as algorithms in a computer friendlyway.

http://ai-depot.com/Intro.html

Artificial Intelligence (AI) is the area of computer science focusing oncreating machines that can engage on behaviors that humansconsider intelligent.

http://library.thinkquest.org/2705/

A tifi i l I t lli (AI) C t ti l Li i ti (CL) Th TALK j t L i ft j t
http://ai-depot.com/Intro.htmlhttp://library.thinkquest.org/2705/http://library.thinkquest.org/2705/http://ai-depot.com/Intro.htmlhttp://find/


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Measuring intelligence: Turing

The Turing Test (1950)

The computer is interrogated by a human via a teletype. It the

test passes if the human cannot tell if there is a computer or

human at the other end.

http://www.cs.cmu.edu/afs/cs/academic/class/15381-s07/www/slides/011607comboIntro.pdf

Is this test sufficient?

http://www.cs.cmu.edu/afs/cs/academic/class/15381-s07/www/slides/011607comboIntro.pdfhttp://www.cs.cmu.edu/afs/cs/academic/class/15381-s07/www/slides/011607comboIntro.pdfhttp://find/


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Measuring intelligence: Searle

The Chinese Room Argument John Searle (1980)

An English man knowing no Chinese locked in a room with Chinesesymbols(a data base)and a book of instruction for manipulating the

symbols(the program)would get Chinese symbols which, unknownto the person in the room, are questions in Chinese (the input). Byfollowing the instruction in the program the man is able to pass outChinese symbols which are correct answers to the questions (theoutput).

The program enables the person in the room to pass the TuringTest for understanding Chinese but he does not understand a word ofChinese.

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Intelligent systems

http://www.cs.cmu.edu/afs/cs/academic/class/15381-s07/www/slides/011607comboIntro.pdf

http://www.cs.cmu.edu/afs/cs/academic/class/15381-s07/www/slides/011607comboIntro.pdfhttp://www.cs.cmu.edu/afs/cs/academic/class/15381-s07/www/slides/011607comboIntro.pdfhttp://find/http://goback/


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Intelligent systems

Key steps of a knowledge-based agent (Craik, 1943):

thestimulusmust be translated into an internalrepresentation

humans sensoric organs vs. machines sensors

therepresentationis manipulated by cognitive processesto derive new internal representations

humans representation? memory

these in turn are translated into action complex with humans sometimes unpredictable

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Complexity and Efficiency

Solving problems

huge computational complexity

Does the intelligent system answer at all? space-time trade-offs

Does the intelligent systems answer in reasonable time?

optimizing the search by use of domain knowledge

heuristics pruning

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Approaches to AI: Classification

Bottom-Up:

the machine will discover the world on its own,the way humans do

Top-Down: learning occurs from what is already known

What is the bottom?

observed data What is the top?

abstractions; data models

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g ( ) p g ( ) p j g p j

Basic tasks

Searching

filtering material of a certain type

Recognizing patterns

abstracting; classifying Constraint solving

satisfying a number of limitations

Reasoning (with uncertain information)

drawing conclusions; deduction; induction

Learning

world changing; maintaining an accurate model; dynamicity

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g ( ) p g ( ) p j g p j

Approaches to AI: Examples

Pattern Recognition

Expert Systems

Human-Computer Interaction

Games

Auction design

Diagnosis

Neural Networks and Parallel Computation

Evolutionary Computation and Planning

Genetic Algorithms

Logic Programming

Robotics


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AI and Computational Linguistics

Computational Linguistics: central role within AI

Automatic Speech Recognition

one of the oldest pattern recognition tasks

Machine-Translation

ETAP-1 Russian English (starting in 1970s) Google services

Human-Machine Interaction

dialogue modeling dialogue systems

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Definitions

Computational linguistics is the science of language with particularattention given to the processing complexity constraints dictated bythe human cognitive architecture. Like most sciences, computationallinguistics also has engineering applications.

http://www.cs.tcd.ie/courses/csll/CSLLcourse.html

Computational linguistics is the study of computer systems forunderstanding and generating natural language.

(Ralph Grishman, Computational Linguistics: An Introduction, Cambridge University Press 1986. )

http://www.cs.tcd.ie/courses/csll/CSLLcourse.htmlhttp://www.cs.tcd.ie/courses/csll/CSLLcourse.htmlhttp://find/http://goback/


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Natural Language Processing (NLP)

Spoken Language

Automatic Speech Recognition (ASR)

Speech Syntesis, e.g., Text-To-Speech (TTS)



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Natural Language Processing (NLP)

Written Language

Natural Language Analysis (NLA)

input utterance abstractions surface form meaning(s)

Natural Language Generation (NLG)

abstraction output utterance

meaning

surface form(s)

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Approaches in CL

Rule-Based explicit encoding of linguistic knowledge

usually consisting of a set of hand-crafted, grammatical

rules

easy to test and debug require considerable human effort

often based on limited inspection of the data with an

emphasis on prototypical examples

often fail to reach sufficient domain coverage often lack sufficient robustness when input data are noisy

http://www.sfs.uni-tuebingen.de/~fr/teaching/ws05-06/icl/slides/lecture2.pdf

http://www.sfs.uni-tuebingen.de/~fr/teaching/ws05-06/icl/slides/lecture2.pdfhttp://www.sfs.uni-tuebingen.de/~fr/teaching/ws05-06/icl/slides/lecture2.pdfhttp://find/


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Approaches in CL

Data-Driven

implicit encoding of linguistic knowledge

often using statistical methods or machine learning

methods

require less human effort

require large-scale data sources

coverage directly proportional to the richness of the data

source

more adaptive to noisy data

http://www.sfs.uni-tuebingen.de/~fr/teaching/ws05-06/icl/slides/lecture2.pdf

http://www.sfs.uni-tuebingen.de/~fr/teaching/ws05-06/icl/slides/lecture2.pdfhttp://www.sfs.uni-tuebingen.de/~fr/teaching/ws05-06/icl/slides/lecture2.pdfhttp://find/http://goback/


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Application Areas

machine translation

speech recognition

speech synthesis

text generation

man-machine interfaces

intelligent word processing: spelling correction, grammar

correction

document management: information retrival, information

extraction, text summarization

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The Project

Talk and Look: Tools for Ambient Linguistic Knowledge

funded by the EU as project No. IST-507802 within the 6th

Framework program

cooperation between

= German Research Center for Artificial Intelligency (DFKI)= University of Saarland, Germany (USAAR)= BOSCH= BMW

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The Aims

focusing on the development of new technologies for

adaptive multimodal and multilingual human-computer

dialogue systems

make dialogue interfaces more conversational, robust,

intuitive, and user-adaptive

Long-term vision: users interacting naturally with devices and

services, in the home or car, using speech, graphics, or a

combination of the two

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The SAMMIE Corpus

Collecting Data

multimodal MP3 player interaction experiment

car driving simulation and interaction with an MP3 player atthe same time

Wizard-of-Oz study

different humanwizardsdecide whether to ask a

clarification request in a multimodal manner or else to use

speech alone



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The Experiment



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The SAMMIE Corpus

Goals

learning policies form multimodal interaction based on

factors such as long vs. short song lists, interaction withthe mp3 player being not the main focus

learning multimodal presentation strategies

learning multimodal clarification strategies

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The SAMMIE System

Developing a prototype of a dialogue system

to show natural, intuitive mixed-initiative interaction particular emphasis on multimodal turn-planning

particular emphasis on flexible natural language generation

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The SAMMIE Multimodal System Architecture

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System Architecture

Description

the classical approach of a pipelined architecture

multimodal fusion and fission modules as parts of the

dialogue manager

dialogue manager decides on the next system move,

based on its model of the tasks, the current context and

the result of the song database

generation of an appropriate message to the driver



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Interaction

A typical interaction with the SAMMIE system



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Multimodality

Screenshot of in-car final showcase systems GUI,main menu

push-to-talk (not barge-in!)

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System integration: How?

Nuance java API for speech recognition

MySQL Database

Dialogue Manager

MARY Text-To-Speech java API for speech syntesis

. . .

Application Programming Interface (API)+

Middleware: The Open Agent Architecture (OAA)

OAA 2.x agent libraries for:

Prolog ANSI C/C++

Java

Compaqs Web Language

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Example of integration

Generic MySQL query OAA agent

connect( host, port, user, pass, database, Result )

Tourist information scenario

retrieve all hotels which are cheaper than 30:

oaa Solve(sendGenericDBQuery( [id, name], [type=hotel,

price


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OAA Example



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The Oahpa! project

OAHPA!

a web-based, language learning program for North Saami

Computer-Assisted/Aided Language Learning (CALL)

http://oahpa.uit.no

http://oahpa.uit.no/http://oahpa.uit.no/http://find/


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North Saami Oahpa!


S S O


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South Saami Oahpa!


Th L i U i


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The Learning Units

Simple learning units

Numra: exercise numerals

Leksa: train vocabulary

MorfaS: train word inflection

More complex learning units

MorfaC: train word inflection in context of a well-formed

sentence

Vasta: give answes to random questions

Sahka: interactive dialogues


Th I f t t


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The Infrastructure

General software

Django: a high-level Python Web framework for rapid

development of Web applications

MySQL database

Javascript for polishing up some web-features not

managable in Django only


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The Infrastructure

Special software

Xerox tools

= twolc: for morphophonology= lexc: for morphology

= xfst: for compiling transducer= lookup: for analysis and generation

Finite-State Morphology Finite-State Automata

VISLCG: parser for Constraint GrammarParsing Syntax Analysis


Th G


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The Games

Options

teaching book word class

dialectal form

level of difficulty


The Games


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The Games

Game Rating

for simple games, just match against the correct answers

from the database

for more complex games,expert knowledgestored in

different modules

= help information on request= XML-file for giving feedback on specific errors

= special parsers for dialogue moves for the dialogue unitSahkabased on Constraint Grammar


Rating vocabulary training
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Rating vocabulary training


Rating dialogue answers


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Rating dialogue answers


Sahka
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Sahka

Overview of the analysis process


Ideas


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Ideas

AI and CL for education projects usefull

reasoning: assumptions about the students knowledge on

a specific subject

specialized databases

language: special terminology; mixed of natural language

and special coding (mathematics, physics, chemistry, etc.)

specialized parsers

Think up your own project!


Conclusion
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Conclusion

= intelligent program a matter of interpretation

= plenty of free software in the web to start your own project

you need an exact view on that it should be about= software integration via API and middleware

= specialised software for natural language input and output

your contribution

your knowledge of a specific domain
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ai-cl_introartificial intelligence: an easy introduction from a computational linguistic perspective

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