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Natural Language Generation for Intelligent Tutoring Systems. Srikanth Ramaka. OVERVIEW. Natural Language Generation Tasks of NLG Systems Natural Language Generation for Intelligent Tutoring System DIAG Experiments. Natural Language Generation.
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Natural Language Generationfor Intelligent Tutoring Systems SrikanthRamaka
OVERVIEW • Natural Language Generation • Tasks of NLG Systems • Natural Language Generation for Intelligent Tutoring System • DIAG Experiments
Natural Language Generation • NLG is concerned with the process of mapping from some underlying representation of information to a presentation of that information in linguistic form (text or speech) . • The Task of NLG is to go from some communicative goal to a text , written or spoken that satisfies this goal. • NLG Systems have been used as interactive explanation tools which communicate information in an understandable way to non-expert users especially in Software Engineering and Medical contexts.
Tasks of NLG Systems • Content Determination and Text Planning : • Content Determination determines “what information should be communicated to the user?” . • Text Planning deals with the organizing the information into a rhetorically structured . • Factors: • Intended Purpose of the text to be generated . • Person to whom it is to be directed . • These tasks are done simultaneously.
Tasks of NLG Systems • Sentence Planning: • Sentence Planner decides how the information will be split among individual sentences and paragraphs. • The Sentence planning includes: • Conjunction and other aggregation • 1) Sam has high blood pressure. Sam has low blood sugar. • 2) Sam has high blood pressure andlow blood sugar. • Pronominalization and other reference • 3) I just saw Mrs. Black. Mrs Black has a high temperature. • 4) I just saw Mrs. Black.Shehas a high temperature. • Adding discourse markers • 5) If Sam goes to the hospital, he should go to the store. • 6) If Sam goes to the hospital, he shouldalsogo to the store.
Tasks of NLG Systems • Realization : • A Realizer generates sentences from ‘deep syntactic’ representation by checking rules of english grammar. • Morphology : • Ex: Plural of box is boxes not boxs . • Agreement : • Ex: Iam here instead of I is here . • Reflexives : • Ex: John saw himself, instead of John saw John.
NLG for ITS • Current Research on Next Generation Intelligent Tutoring System . • ITS that teaches students how to troubleshoot mechanical systems. • Focus is on the sentence planning and on aggregation.
DIAG • DIAG is a shell to build ITS. • Builds on VIVIDS authoring environment. • DIAG Tutoring strategy steers the students towards performing the tests that has greater potential for reduced uncertainty. .
Language Generation for DIAG • What DIAG application presents a student? • Student Interaction with DIAG • DIAG helps in refining the solution
Information on “Consult Indicator” Query in DIAG-ORIG • The Visual combustion check is igniting which is abnormal in this • startup mode (normal is combusting). • Oil Nozzle always produces this abnormality when it fails. • Oil Supply Valve always produces this abnormality when it fails. • Oil pump always produces this abnormality when it fails. • Oil Filter always produces this abnormality when it fails. • System Control Module sometimes produces this abnormality when it fails. • Ignitor Assembly never produces this abnormal indication when it fails. • Burner Motor always produces this abnormality when it fails. • And , maybe others affect this test.
The Sentence Planner in DIAG-NLP1 • Tutoring Strategy is not changed. • Functional Aggregation. • EXEMPLARS. • Text Planar.
Information on “Consult Indicator” Query in DIAG-NLP1 • The visual combustion check indicator is igniting which is abnormal in startup mode. Normal in this mode is combusting. • Within the Oil Burner • These RU always produce this abnormal indication when they fail: • Oil Nozzle; • Oil Supply Valve; • Oil pump; • Oil Filter; • Burner Motor. • The Ignitor assembly replaceable unit never produces this abnormal indication when it fails. • Within the furnace system, • The System Control Module RU sometimes produces this abnormal indication when it fails. • Also, other parts may affect this indicator.
EXEMPLAR • Aim of Exemplar . • Developed in Java 1.1 . • Object Oriented Rule Based Generator . • Description EXEMPLARS • - DescribePart • - DescribeIndicator • - DescribeReplUnit • - DescribeEmptyList • Aggregation EXEMPLARS • - AggByType: 6 exemplars • - AggByContainer • - AggByFufer • - AggByState
Working of Exemplars • DIAG collects the information it needs to communicate with the student . • The textfile contains information as • <DIAG event-name><Indicator|ReplUnit> • <attribute1-name> <attribute1-value> • . . • . . • <attributeN-name> <attributeN-value>
Example of TextFile • ConsultIndicator Indicator • Name Visual Combustion check • State igniting • modeName startup • normalState combusting • ConsultIndicator ReplUnit • Name Oil Nozzle • Fufer always • ConsultIndicator Indicator • Name Ignitor assembly • Fufer no effect • ConsultIndicator ReplUnit • Name System Control Module • Fufer sometimes
Task of EXEMPLARS • Determination of specific exemplars . • Adds the chosen exemplars to the sentence planner . • Linearizes and lexicalizes the feedback in its final form.
Simple EXEMPLAR • Exemplar DescribeIndicator( Vector lists, int index, String tense ) extends DescribePart • { • boolean evaIConstraints() • { return ((Part)lists.elementAt(index) instanceof Indicator); } • void apply(){ • Indicator ind = (Indicator)lists.elementAt(index); • <<+The {ind.getName()} indicator {tense} {ind.getState()}+>> • If ((ind.getStafe()).equals(ind.getNormalState())) • <<+ which is normal in {ind.getMode()} mode. + >> • Else • <<+ which is abnormal in {ind.getMode()} mode. Normal in this mode is {ind.getNormalState()}.+>> • } • }
Revisited information on “Consult Indicator” Query in DIAG-NLP1 • The visual combustion check indicator is igniting which is abnormal in startup mode. Normal in this mode is combusting. • Within the Oil Burner • These RU always produce this abnormal indication when they fail: • Oil Nozzle; • Oil Supply Valve; • Oil pump; • Oil Filter; • Burner Motor. • The Ignitor assembly replaceable unit never produces this abnormal indication when it fails. • Within the furnace system, • The System Control Module RU sometimes produces this abnormal indication when it fails. • Also, other parts may affect this indicator.
Response Aggregation • Dimension values of Aggregation : • Subsystem, Level of Certainty . • Limitations : Loss of Rhetorical Relations . Complexity of text increases may mislead the student .
Second Prototype DIAG-NLP2 • Same Aggregation Structure . • Rhetorical Relations such as contrast with bottom-up fashion . • SNePS Knowledge Representation and Reasoning System . • Fewer Dimensional Values for aggregation . • Referential Expressions using GNOME Algorithm .
Information on “Consult Indicator” Query in DIAG-NLP2 • The oil flow indicator is not flowing in startup mode. • This is abnormal. • Normal in this mode is flowing. • Within the Furnace System, • this is sometimes caused if • the system control module has failed . • Within the Oil burner, • this is never caused if the ignitor assembly has failed . • In contrast , this is always caused if • the burner motor , oil filter , oil pump, oil supply valve , or oil nozzle has failed .
Observation with Human Consulting • Human Generated advice : • 1. Referring to oil nozzle, supply valve, pump, filter, etc: • “…check the other items on the fuel line “ . • 2. Referring to all the burner parts : • “…consider the units that are involved with heating the water” . • 3. Referring to the photocell that senses the presence of flames: • “Check the electronics that indicates that there is combustion” .
Third Prototype DIAG-NLP3 • NLG System is coupled with RealPro . • RealPro is a grammar rule engine. • RealPro performs syntactic and lexical realization .
Information on “Consult Indicator” Query in DIAG-NLP3 • The Combustion is abnormal. • In the oil burner, check the units along the path of the oil and the burner motor.
References • Ehud Reiter : Building Natural Language Generation Systems • Robert Dale and Chris Mellish : Towards the Evaluation of Natural Language Generation . • Barbara Di Eugenio, Michael Glass , Michael J. Trolio: THE DIAG experiments: Natural Language Generation for ITS . • Barbara Di Eugenio, Michael Glass , Michael J. Trolio , Susan Haller: Simple Natural Language Generation and ITS . • Barbara Di Eugenio, Michael Glass, Susan Haller: Development and Evaluation of NL interfaces in a Small Shop • Barbara Di Eugenio, Michael J. Trolio : Can Simple Natural Language Generation improve Intelligent Tutoring Systems?