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Information Extraction Tools and Methods for Understanding Dialogue in a Companion R. Catizone, A. Dingli, H. Pinto and Y.Wilks University of Sheffield. LREC 2008 Morocco. Senior Companion. Developing a range of companions to assist the elderly. Photos. News. Yellow pages.

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LREC 2008 Morocco

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Lrec 2008 morocco

Information Extraction Tools and Methods for Understanding Dialogue in a CompanionR. Catizone, A. Dingli, H. Pinto and Y.WilksUniversity of Sheffield

LREC 2008

Morocco


Senior companion

Senior Companion

Developing a range of companions to assist the elderly

Photos

News

Yellow pages


Information extraction tools and methods for understanding dialogue in a companion r catizone a dingli h pinto and 1324743

The Senior Companion is about…


Information extraction tools and methods for understanding dialogue in a companion r catizone a dingli h pinto and 1324743

Multimodal dialogue for building a picture of…

…through his photographs

the user


Information extraction tools and methods for understanding dialogue in a companion r catizone a dingli h pinto and 1324743

Behind the scenes using the dialogue to

…tag photos

Octavia

family holiday

Venice

2007

summer


Photos

Photos

  • A Photograph application for reminiscing about personal photos.

  • The user begins with a set of photos which will be input into the system in advance.

  • The system engages in conversation with the user about the photos.

  • The system will create a life narrative by extracting key facts from the user about his/her photos and the people, places and events that are represented.

  • The information about the photos can later be retrieved and displayed in a future user session.

  • Semantic features extracted during the dialogue will be automatically associated with the photos ( may include audio recordings)


Life narrative through photos

Life Narrative through Photos

  • Life Narrative - Dynamically builds segments of a person’s life that correspond to relationships with:

    • People and Places with respect to

      • Life events (Birthdays, marriages, etc)

      • Journeys and Travels

      • Special memories


Information extraction tools and methods for understanding dialogue in a companion r catizone a dingli h pinto and 1324743

News

  • News reading

  • Live from BBC RSS feeds

  • Retrieved through categories

    • Sports, business, international, etc.


Yellow pages future

Yellow pages (future)

  • To incorporate helping with everyday interactions such as yellow pages assistant for finding information.

  • Example for finding a plumber

    User: “Hello Morgan, my toilet is broken, I really need to find a plumber. Can you help me.”

    System: “Sure I can,the broken toilet is at your home on Hayfield Rd right?

    User: “Yes”

    System: ”Let me have a look and see what I can find.”

    User: “Ok”

    System: “Yes, I’ve found a local plumber in your area. Would you like me to get him on the phone using Skype.”

    User: “Yes please”

    System: “Ok here you go”


Information extraction tools and methods for understanding dialogue in a companion r catizone a dingli h pinto and 1324743

The System


Information extraction tools and methods for understanding dialogue in a companion r catizone a dingli h pinto and 1324743

Interface Layer

Fusion

Napier Interface

Avatar

Input Queue

DialogAct

Tagger

NLU

GATE

TripleStore

Dialog

Manager

NLG

Senior Companion Architecture

Output Manager


Modules

Modules

  • Interface + Avatars

  • Face identification (Open CV)

  • Fusion

  • ASR

  • Natural Language Understanding

  • Dialogue Manager

  • Knowledge Representation/Reasoning

  • TTS


Interface

Interface

  • Multimodal - speech and touch (using a touchscreen tablet)

  • Includes photos, avatar and text box

  • Currently designed to discuss user’s photos and read news.

  • Displays multiple photos at a time

    • Photo management

    • Photo selection


Avatars

Avatars

  • Experimenting with different avatars

    • Morgan (AAA)

    • Crazy Talk

      • Woman

      • Ken Dodd

      • Lion

      • A computer


Interface 2

Interface (2)


Interface 21

Interface (2)

Please read me the news


Opencv

OpenCV

  • Face identification software

    • Finds the coordinates of all of the faces in a photo.

    • Cannot recognize the same face in more than one photo, but investigating face recognition software: Polar Rose


Photo applications

Photo Applications

  • Also use digital photo metadata such as

    • Date and time

    • GPS coordinates

  • Photos that have annotations (Facebook)

More information to start makes for more interesting dialogue


Information extraction tools and methods for understanding dialogue in a companion r catizone a dingli h pinto and 1324743

SamuelCarter

ThomasClark

Ed Bloch

Olivia Ford


Fusion

Fusion

  • Merges speech and pointing input

“This is my daughter Octavia”


Information extraction tools and methods for understanding dialogue in a companion r catizone a dingli h pinto and 1324743

ASR

  • Dragon Naturally Speaking

    • requires 10 minute training session

    • accuracy is high - up to 99%

    • application integrated with the SC

    • full integration of the code using the Nuance SDK planned for next version


Natural language understanding

Natural Language Understanding

  • Sentence splitting

  • POS tagger

  • Parser

  • Annie Named Entity Recognizer (GATE, USFD)

    • person names (10,000)

    • locations (Web trawl)

    • 60 relation names

      • mother, brother, sister, friend, etc.

  • Dialogue Act Tagger (ALB).

  • Populates Ontology instances.

  • To use an IE approach for semantic interpretation


Nlu the task so far

NLU: The task so far …

  • Identify people and their relationships to the user and each other

  • Identify locations


The process

The process

Utterance

Gate

Dialogue Act Tagger

Annie

Syntactic Parser

Ontology Processing

Syntactic and Semantic representation


Information extraction tools and methods for understanding dialogue in a companion r catizone a dingli h pinto and 1324743

Noun Chunks

Verb Phrases

PRP VBD TO VB NNP PRP$ JJ NN RB IN PRP$ NN NNP

Gate

I went to meet Lynne, my old flatmate, together with my sister Francesca.


Annie

Relationships

Persons

Annie

I went to meet Lynne, my old flatmate, together with my sister Francesca.


Syntactic parser

Syntactic Parser

I went to meet Lynne, my old flatmate, together with my friend Francesca.

Who is the sister and who is the flatmate?

((((I))((went)(((to)(meet)(Lynne)(,)(my)(old)(flatmate)(,))

( (together))((with)( (my)(sister)(Francesca))))))(.)))

(((I)(went((to meet Lynne , my old flatmate,)

( together)(with( my sister Francesca))))).))

(((I)(went((to meet Lynne , my old flatmate,)

( together)(with( my sister Francesca))))).))

Syntactic bindings will help us identify that!


Ontology

Ontology


Reasoning

Reasoning

  • Allows us to infer new knowledge

    • Sister(My) = Francesca

    • Flatmate(My) = Lynne

    • If we have in the ontology:

      • Mother(My) = Mary

    • We can infer through the properties of the Sister relationship that

      • Mother(Francesca) = Mary

      • Daughter(Mary) = Francesca


Knowledge base

Knowledge Base

  • Relationships Ontology

    • Almost 40 person classes (mother, father, etc)

    • Almost 40 relationships (has friend, etc)

      Will be adding more …

  • Locations Ontology

    • Most continents, countries, regions, cities

    • Plus

      • several places of interests per region

      • top 10 things to do in cities

        Will be adding more …


Information extraction tools and methods for understanding dialogue in a companion r catizone a dingli h pinto and 1324743

DAMSL Dialogue Act set

* ASSERT ("This is my sister". Yes and no are also considered asserts, where they are responses to yes/no questions, thus abbreviated assertions.) * OFFER ("Shall we look at another picture?") * COMMIT ("Okay I'll do that") * EXPRESSION (All social expressions such as "you're welcome". Also things like "wow!" and "great!") * INFORMATION REQUEST (open question) * CONFIRMATION REQUEST (yes/no question) * REPEAT REQUEST ("Pardon?") * ACTION DIRECTIVE ("Show me another one." All imperatives.) * OPEN OPTION ("We could look at another picture." Stating an option in a way that doesn't demand an answer.) * OPENING ("Hi") * CLOSING ("Goodbye") * ANSWER (An answer is invariably also an assert. Yes/no answers are asserts.) * BACKCHANNEL ("Uhuh") * REPEAT REPHRASE (Expressing understanding by paraphrasing) * COMPLETION (Completing the utterance of the other speaker) * NON-UNDERSTANDING ("I don't understand") * CORRECTION (An assertion that corrects a previous assertion) * ACCEPT (Accepting a proposal) * REJECT (Rejecting a proposal)


Semantic specification

Semantic specification

  • Each dialogue begins with a user object and a picture object

    • User/Person objects have

      • name, people relations,age

    • Picture object

      • location, occasions, people, dates)

User object

Name

Relations

Age

Person: Zoe

Location

Occasion

People

Occasion: Hilary Duff concert

Picture object

Location: Birmingham

Person: Roisin, snooks


Information extraction in the sc

Information Extraction in the SC

  • Why do we want to use IE for Semantic representation?

    User utterances are unstructured

  • Using GATE IE tools to create templates

    • Relationship between the Named Entities and the significant Events.

      • Categorize events into meaningful classes


Information extraction 2

Information Extraction (2)

  • Example sentence

    ‘That is my daughter Zoe on the right’

  • Simple Example template :

    Relation

    • Person1 Related-to Person2

      • Related-to : is-relation

        • is-relation: is-daughter, is-mother etc.

          is-daughter: lexical string: ‘is my daughter’

  • Filled IE Template

    Relation : [Person1=‘Zoe’], [Person2=‘Roberta’], [Related-to=is-daughter]


  • Sc ontology

    SC Ontology

    SC Ontology for Inference

    “Here are my daughters, Zoe and Octavia in New York City”

    Infer using the family relations Ontology that:

    -Zoe and Octavia are sisters

    -Roberta is the mother of Octavia and Zoe


    Dialogue manager 1

    Dialogue Manager (1)

    • Manages discussion of

      • User’s photos with respect to

        • Location

        • Time it was taken

        • Occasion

        • People in the Photo (exploits positional information of people)

          • Name

          • Age

          • Relation to the user

      • Photo Management

        • “Show me all the photos of my mother”

        • Photo selection

      • News

        • Reading and stopping

        • Choice of politics, sport or business


    Dialogue manager 2

    Dialogue Manager (2)

    • Accepts pre-annotated photos to allow the system to engage in more interesting conversation more quickly.

    • Handles basic photo management tasks:

      “Show me all the photos of my mother”

      “Please move on to the next photo”

      • Responds when same person is mentioned in more than one photo (using the user’s name)

    • Remembers user information from multiple sessions

    • Generation : template based


    Dialogue manager 3

    Dialogue manager (3)

    • Dialogue Manager adapted from COMIC DM

      • General purpose control structure that does the dialogue planning

      • Stack based system

        • Conforms to common behaviour of conversation: Discuss topic 1, move to topic 2, go back to topic 1.

      • Augmented transition networks, called Dialogue Action Forms (DAFs) for handling domain sub-tasks.

      • References the Dialogue History, Knowledge base and the User Model


    Dialogue action forms

    Dialogue Action Forms

    • GUI editor for creating DAFs

    • Composed of nodes and arcs containing tests and actions

    • DAFs pre-stacked, but can be overidden by matching indexing terms (semantic classes, significant words)

      • Essential for mixed-initiative conversation


    Sc dialogue manager stack

    SC Dialogue Manager Stack

    Run Greeting DAF

    Pop greeting DAF

    Push photo DAF

    Run people DAF

    Pop people DAF

    System start

    Run photo DAF

    People DAF

    name

    age

    people

    relation

    event

    event

    date

    date

    occasion

    occasion

    greeting

    photo

    location

    location

    goodbye

    goodbye

    goodbye

    goodbye


    Dafs 1

    DAFs (1)


    Machine learning in the senior companion

    Machine Learning in the Senior Companion

    • Using first Senior Companion prototype to generate more data, augmenting WoZ data gathered by NAP and AAA and hand annotated.

    • Plan to “tile” (as in Hearst) the data to seek segmentations corresponding to topics and dialogue moves.

    • Plan to generalise across a set corresponding to “same” topic or move to generate a draft Dialogue Action.


    Www companions project org

    www.companions-project.org


    Thank you

    Thank You


    Evaluation of the senior companion

    Evaluation of the Senior Companion

    • Accuracy

      • Is the information that the system discusses accurate

        • Important when the user returns for repeat sessions (system needs to remember and recall collected information at the appropriate time).

      • Does the system discuss things in an efficient way? (not ask for clarification when the information is already known)

    • User satisfaction

      • Self-assessment: May include some testing of the user’s level of contentment with the system while running.


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