Natural Language Agents Elaine Rich. What do they Need? The linguistic facts A KR system that is rich enough to encode everything we can talk about Understanding:

Natural Language Agents

Elaine Rich

What do they Need?

The linguistic facts

A KR system that is rich enough to encode everything we can talk about

Understanding: Figuring out what the speaker meant and what you should do about it

Generation: Figuring out what to say to achieve your goals

Turing’s Prediction

1950 Turing predicted that in about fifty years "an average interrogator will not have more than a 70 percent chance of making the right identification after five minutes of questioning".

Was Turing Right?

>What's the best place to eat Chinese food?

ALICE: My favorite places are California and Maine. Holland is cool too.

>What do you like about Maine?

ALICE: The special effects are the best.

Another Application - MT

Austin Police are trying to find the person responsible for robbing a bank in Downtown Austin.

El policía de Austin está intentando encontrar a la persona responsable de robar un banco en Austin céntrica.

The police of Austin is trying to find the responsible person to rob a bank in centric Austin.

MT - The LimitsA Florida teen charged with hiring an undercover policeman to shoot and kill his mother instructed the purported hitman not to damage the family television during the attack, police said on Thursday.

Un adolescente de la Florida cargado con emplear a un policía de la cubierta interior para tirar y para matar a su madre mandó a hitman pretendida para no dañar la televisión de la familia durante el ataque, limpia dicho el jueves.

An adolescent of Florida loaded with using a police of the inner cover to throw and to kill his mother commanded to hitman tried not to damage the television of the family during the attack, clean said Thursday.

MT - The Limits

I have a dream, that my four little children will one day live in a nation where they will not be judged by the color of their skin but by the content of their character. I have a dream today – Martin Luther King

I am a sleepy, that my four small children a day of alive in a nation in where they will not be judged by the color of its skin but by the content of its character. I am a sleepy today. (Spanish)

http://www.shtick.org/Translation/translation47.htm

Going Both Ways

Notice that both of these applications require that we process language in two directions:

•Understanding

•Generation

But also notice that it is possible to do a somewhat passable job without going through any meaning representation.

When Meaning is Critical

English: Put the kid’s cereal on the bottom shelves.

Javaimport java.util.ArrayList;public class GroceryStore{ private int[][][] shelves; private ArrayList products; public void placeProducts(String productFile) { FileReader r = new FileReader(productFile); GroceryItemFactory factory = new GroceryItemFactory(); while(r.hasNext()) products.add( factory.createItem(r.readNext())); ThreeDLoc startLoc; GroceryItem temp; for(itemNum = 0; itemNum < products.size(); itemNum++) { temp = (GroceryItem)(products.get(itemNum)) startLoc = temp.getPlacement(this); shelves[startLoc.getX()][startLoc.getY()][startLoc.getY()]= tempgetIDNum(); } }}

Java, Continued

public class ChildrensCereal extends GroceryItem{ private static final int PREFERRED_X = -1; private static final int PREFERRED_Y = 0; private static final int PREFERRED_Z = 0; public ThreeDLoc getPlacement(GroceryStore store) { ThreeDLoc result = new ThreeDLoc(); result.setX(store.find(this)); result.setY(PREFERRED_Y); result.setZ(PREFERRED_Z); return result; }}

It’s All about Mapping

What Are We Going to Map to?

English: Do you know how much it rains in Austin?

Months

Month

Days

The database:

RainfallByStation

year

month

station

rainfall

Stations

station

City

English: What is the average rainfall, in Austin, in months with 30 days?

SQL:

SELECT Avg(RainfallByStation.rainfall) AS AvgOfrainfall FROM Stations INNER JOIN (Months INNER JOIN RainfallByStation ON Months.Month =

RainfallByStation.month) ON Stations.station =

RainfallByStation.stationHAVING (((Stations.City)="Austin") AND ((Months.Days)=30));

Designing a Mapping Function for NL Understanding

•Morphological Analysis and POS tagging* The womans goed home.

•Syntactic Analysis (Parsing)* Fishing went boys older

•Extracting MeaningColorless green ideas sleep furiously.Sue cooked. The potatoes cooked. * Sue and the potatoes cooked.

•Putting it All in ContextMy cat saw a bird out the window. It batted at it.

•What isn’t Said Winnie doesn’t like August. He doesn’t like melted ice cream.

Ambiguity – the Core Problem

•Time flies like an arrow.•I hit the boy with the blue shirt (a bat). •I saw the Grand Canyon (a Boeing 747)

flying to New York.•I know more beautiful women than Kylie.•The boys may not come.•I only want potatoes or rice and beans.•Is there water in the fridge?•Who cares?•Have you finished writing your paper? I’ve written the outline.

Morphological Analysis and POS Tagging

Morphological Analysis:

played = play + ed = play (V) + PAST

saw = see (V) + PAST

leaves =





leaves = leaf (N) + PL

= leave (N) + PL

= leave (V) + 3rdS

compute





leaves = leaf (N) + PL

= leave (N) + PL

= leave (V) + 3rdS

compute computer computerize computerization

POS Tagging:

I hit the bag.

Morphological Analysis Using a Finite State Transducer

Stochastic POS Tagging

)(

)()|(

)|(

wP

contextintPcontextintwP

wcontextintP

ii

i

Naïve Bayes Classification: Choose the POS tag that is most likely for the current word given its context. For example:

Secretariat expected to race tomorrow.

Using Bayes Rule

We want to choose the tag tj with maximum likelihood:

)|()|(maxarg 1 jiii

j ttPtwPt

The Importance of Parsing Even When We’re Not Doing Full Understanding

Find me all the:

Lawyers whose clients committed fraud

vs

Lawyers who committed fraud

vs

Clients whose lawyers committed fraud

Parsing - Building a Tree

John hit the ball. S

NP VP

N V NP

John hit DET N

the ball

(S (NP (N John))

(VP (V hit)

(NP (DET

the)

(N

ball))))

Grammar Rules

We can build such a parse tree using a grammar with rules such as:

S NP VPNP NVP V NP

The Lexicon is Important

* The cat with a furry tail purred a collar.

Mary imagined a cat with a furry tail.

Mary decided to go.

* Mary decided a cat with a furry tail.

Mary decided a cat with a furry tail would be her next pet.

Mary gave Lucy the food.

* Mary decided Lucy the food.

Mary asked the cat.

Mary demanded a raise.

Mary asked for a raise.

Parsing: Dealing with Ambiguity

Water the flowers with brown leaves.

Water the flowers with the hose.

English:

Using Domain Knowledge

(plant (isa living thing))

(flower (isa plant)

(has parts leaf))

(water (isa action)

(instrument mustbe container))

(hose (isa container))

A Harder One

John saw a boy and a girl with a red wagon with one blue and one white wheel dragging on the ground under a tree with huge branches.

How Bad is the Ambiguity?•Kim (1) •Kim and Sue (1) •Kim and Sue or Lee (2) •Kim and Sue or Lee and Ann (5) •Kim and Sue or Lee and Ann or Jon (14) •Kim and Sue or Lee and Ann or Jon and Joe (42) •Kim and Sue or Lee and Ann or Jon and Joe or Zak (132) •Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel (469) •Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel or Guy (1430) •Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel or Guy and Jan (4862)

The number of parses for an expression with n terms is the n’th Catalan number:

1

22)(

n

n

n

nnCat

Parsing: Gapping

English: Who did you say Mary gave the ball to?

Sentences like this make specifying the grammar difficult. They also make it hard to use a simple, context-free parser.

Semantics: The Meaning of Words

Getting it right for the target application:

“month” RainfallByStation.month

Dealing with ambiguity:

“spring” or or

“stamp” or

Olive oil

Semantics: The Meaning of Phrases

Semantics is (mostly) compositional.

(oil (made-from olives))

Occasionally It Isn’t

Olive oil

But Usually It Is

Peanut oil

(oil (made-from peanuts))

Another One

Coconutoil

(oil (made-from coconut))

But What About This One?

Baby oil

But What About This One?

Baby oil

(oil (used-on baby))

And Another One

Cooking oil

(oil (used-for cooking))

And Another One

Riding jacket

Leather jacket

Letter jacket

Rain jacket

Idioms Don’t Work This Way

•I’m going to give her a piece of my mind.

•He bent over backwards to make the sale.

•I’m going to brush up on my Spanish.

Putting Phrases Together

Bill cooked the potatoes.

The potatoes cooked in about an hour.

The heat from the fire cooked the potatoes in 30 minutes.

(cooking-event (agent )

(object )

(instrument )

(time-frame )

* Bill and the potatoes cooked.

Language at its Most Straightforward –Propositional Content

•Bill Clinton was the 42nd president of the United States.

•Texas is in France.

•The Matrix is playing at the Dobie.

•Lunch is at noon.

•What time is it?

When There’s More - Presuppositions

•What is Clinton famous for?

•Where’s The Matrix playing?

•Who is the king of France?

•Have you started making it to your morning classes?

•I’m going to check out all the five star restaurants in Cleveland on this trip.

Coherence

Winnie doesn’t like melted ice cream. He always dreads August.

* Winnie doesn’t like melted ice cream. He always dreads January.

Winnie wanted to go to the store. He went to find Christopher Robin.

* Winnie wanted to go to the store. He counted quickly to 10.

Winnie walked into the room. Christopher Robin looked up and smiled.

* Winnie walked into the room. The earth rotates around the sun.

We Can’t Say it All

Christopher Robin and Winnie decided to go out for lunch. They remembered that Coji’s doesn’t have hot dogs on Saturdays, so they went to Buzzy’s. They got their food, slathered on the mustard, and walked home.

Conversational Postulates

Grice’s maxims:

•The Maxim of Quantity:Be as informative as required.Don’t be more so.

•The Maxim of Quality: Do not say what you believe to be false.Do not say that for which you lack sufficient evidence.

•Maxim of relevance: Be relevant

•Maxim of manner:Avoid obscurity of expressionAvoid ambiguityBe brief.Be orderly.

Conversational Postulates and Scalar Implicature

A: Have you done the first math assignment yet?

B: I’m going to go buy the book tomorrow.

Another Example of Scalar Implicature

A: When did you get home last night?

B: I was in bed by midnight.

When There’s More – Conversational Postulates and Inference

A: Joe doesn't seem to have a girl-friend these days.

B: He's been going to Dallas a lot lately.


A: Let’s go to the movies tonight.

B: I have to study for an exam.


Reviewer of new book:

It is well-bound and free of typographical errors.


A: What do you think of my new dress?

B: It’s interesting.

When There’s More – Conversational Postulates and Illocutionary Force

•Do you know what time it is?



•What time is it?



•What time is it?

•I’m freezing.



•What time is it?

•I’m freezing.

•Get up and go close the window.



•What time is it?

•I’m freezing.

•Get up and go close the window.

Politeness

What About Applications Where Almost is OK?

•Searching the web–Leaving some of the work for people

–Retrieval failures are ok

www.google.com

•Snooping

http://www.google.com/index.html



Going the Other Way: Generation

(c (isa cooking-event) (agent x ) (object y) (instrument z) (time-frame ))(x (isa man) (name Bill) (height 6') (attire (head- covering h)) (born-location b))

(y (some-of potatoes) (type-of Idaho)) (maturity new))(z (isa microwave) (brand Sharp))(h (isa gimme) (color red))(gimme (subclass hat))(b (isa city) (name Austin))

One Problem - What is Reusable?

Because we’re dealing with a mapping problem, we can’t build a definitive, useful for all applications, NL interface. However, we can build:•Lexicons that contain morphological and POS information, as well as meanings given in terms of standard KBs such as Wordnet.•Morphological analyzers•Syntactic parsers that appeal to a statistical and/or semantic filter through a common interface•Inference engines•Dialogue management systems that handle linguistic-level issues and plug into domain-specific knowledge

http://www.cogsci.princeton.edu/%7Ewn/

Spoken Language - Understanding

0 2 4 6 8 10 12

x 104

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

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The dis crete

Fourier

trans form of a real sig nal

val ued

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sym me

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Spoken Language - Generation

The issues:

•Figuring out what to say (same as for written language)

•Pronouncing words

•Linking them together

•Getting the prosody right

A Recent Loebner Prize Winner

> Why do you think people want to go to the moon?

Jabberwacky: You don't. Plastic and metal, perhaps. No flesh.

> Can you imagine life before plastic?

Japperwacky: Can you?

Sure. Milk came in glass bottles.

Jabberwacy: You can come here whenever you want. Iwill welcome you.

Documents

Natural Language Agents Elaine Rich. What do they Need? The linguistic facts A KR system that is rich enough to encode everything we can talk about Understanding: