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Natural Language Generation for Intelligent Tutoring Systems Srikanth Ramaka.

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Presentation on theme: "Natural Language Generation for Intelligent Tutoring Systems Srikanth Ramaka."— Presentation transcript:

1 Natural Language Generation for Intelligent Tutoring Systems Srikanth Ramaka

2 OVERVIEW Natural Language Generation Tasks of NLG Systems Natural Language Generation for Intelligent Tutoring System DIAG Experiments

3 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.

4 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.

5 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 and low blood sugar.  Pronominalization and other reference  3) I just saw Mrs. Black. Mrs Black has a high temperature.  4) I just saw Mrs. Black. She has 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 should also go to the store.

6 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: I am here instead of I is here.  Reflexives :  Ex: John saw himself, instead of John saw John.

7 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.

8 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..

9 Language Generation for DIAG  What DIAG application presents a student?  Student Interaction with DIAG  DIAG helps in refining the solution

10 DIAG Application on Home Heating

11 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.

12 The Sentence Planner in DIAG-NLP1  Tutoring Strategy is not changed.  Functional Aggregation.  EXEMPLARS.  Text Planar.

13 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.

14 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

15 Working of Exemplars  DIAG collects the information it needs to communicate with the student.  The textfile contains information as  .. 

16 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

17 Task of EXEMPLARS  Determination of specific exemplars.  Adds the chosen exemplars to the sentence planner.  Linearizes and lexicalizes the feedback in its final form.

18 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); > If ((ind.getStafe()).equals(ind.getNormalState())) > Else > }

19 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.

20 Response Aggregation  Dimension values of Aggregation :  Subsystem, Level of Certainty.  Limitations : Loss of Rhetorical Relations. Complexity of text increases may mislead the student.

21 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.

22 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.

23 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”.

24 Third Prototype DIAG-NLP3  NLG System is coupled with RealPro.  RealPro is a grammar rule engine.  RealPro performs syntactic and lexical realization.

25 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.

26 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?


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