Executable Semantic Parsing - Tel Aviv Universityjoberant/teaching/class1_index.pdf · SHRDLU...

Executable Semantic Parsing

ESSLLI

Aug 22, 2016

Instructor: Jonathan Berant

Oct 2014

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1B dollars please

2

Why is this an important language problem?

what is the second biggest city in california that is not near a river?

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• You can not memorize this

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• You can not memorize this

• Understand language atoms

• Understand how to compose them

• Generalize to any utterance

• Collect evidence from multiple sources

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Executable semantic parsing

Which states’ capitals are also their largest cities by area?

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semantic parsing

µx.Type.USState u Capital.argmax(Type.City u ContainedBy.x,Area)

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semantic parsing


execute logical form

Arizona,Hawaii,Idaho,Indiana,Iowa,Oklahoma,Utah

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semantic parsing



Arizona,Hawaii,Idaho,Indiana,Iowa,Oklahoma,Utah

Execute query against knowledge-base

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Send a reminder to all students who haven’t turned in their homework.

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semantic parsing

∀x ∈ (Student u ¬Submit.Homework) : Remind(x)

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semantic parsing



[50 emails sent]

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semantic parsing



[50 emails sent]

Execute command against personal assistant

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[utterance: user input]

semantic parsing

[program]


[behavior: user output]

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[utterance: user input]

semantic parsing

[program]


[behavior: user output]

Execution connects language with the world

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Outline

• Introduction (30 min.)

– Applications

– Background

– Short history

– Goals and plan

• Representation (30 min.)

– Worlds

– Simply-typed lambda calculus

– Lambda-DCS

• Exercise (30 min.)

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Outline


– Applications

– Background

– Short history

– Goals and plan


– Worlds


– Lambda-DCS


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Personal assistants everywhere!

Show me photos from Utah last august

Find videos I took at Ive’s birthday party

What movies are playing today?

. . .

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Alexa, Play some Christmas music

Alexa, turn the bedroom lights on

Alexa, what’s my commute?

. . .

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Can you help me order flowers for my mom’s birthday

Where’s the best place to go hiking in the bay area

Is there a dog friendly beach nearby?

Remind me to congratulate Tanya the next time she calls

. . .

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Personal assistant everywhere!

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Question answering

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Robot control

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Research assistants

Which country has the highest CO2 emissions?

Which had the highest increase since last year?

What fraction is from the five countries with highest GDP?

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Outline


– Applications

– Background

– Short history

– Goals and plan


– Worlds


– Lambda-DCS


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semantic

parsing

language logic

learning

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semantic

parsing

language logic

learning

But executable!

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Non-executable semantic parsing

The boy wants to go

[Banarescu et al., 2013]

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Non-executable semantic parsing

The boy wants to go

No environment to execute semantic representation

[Banarescu et al., 2013]

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Semantics

Sentiment analysis, Semantic Role Labeling, Named Entity Recognition, ...

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Semantics

Sentiment analysis, Semantic Role Labeling, Named Entity Recognition, ...

Semantic parsing: Full meaning representation

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Outline


– Applications

– Background

– Short history

– Goals and plan


– Worlds


– Lambda-DCS


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SHRDLU [1971]

Person: Pick up a big red block.

Computer: OK.

Person: Grasp the pyramid.

Computer: I don’t understand which pyramid you mean.

Person (changing their mind): Find a block which is taller than the oneyou are holding and put it into the box.

Computer: By ”it”, I assume you mean the block which is taller thanthe one I am holding.

Computer: OK.

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SHRDLU [1971]

Person: Pick up a big red block.

Computer: OK.

Person: Grasp the pyramid.

Computer: I don’t understand which pyramid you mean.

Person (changing their mind): Find a block which is taller than the oneyou are holding and put it into the box.

Computer: By ”it”, I assume you mean the block which is taller thanthe one I am holding.

Computer: OK.

Person: What does the box contain?

Computer: The blue pyramid and the blue block.

Person: What is the pyramid supported by?

Computer: The box.

Person: How many blocks are not in the box?

Computer: Four of them.

Person: Is at least one of them narrower than the one which I told youto pick up?

Computer: Yes, the red cube.22

Other grammar-based approaches

Lunar (1972):

List the rocks that contain chromite and ulvospinel

What minerals have been identified in the lunar samples?

What is the average analysis of Ir in rock S10055?

Give me the K / Rb ratios for all lunar samples

Chat-80 (1982):

Does Afghanistan border China?

How many countries does the Danube flow through?

What is the capital of each country bordering the Baltic?

Which country is bordered by two seas?

Woods et al., 1972; Warren and Pereira, 1982

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Other grammar-based approaches

Lunar (1972):

List the rocks that contain chromite and ulvospinel

What minerals have been identified in the lunar samples?

What is the average analysis of Ir in rock S10055?

Give me the K / Rb ratios for all lunar samples

Chat-80 (1982):

Does Afghanistan border China?

How many countries does the Danube flow through?

What is the capital of each country bordering the Baltic?

Which country is bordered by two seas?

Problem: does not scale!

Woods et al., 1972; Warren and Pereira, 1982

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Statistical semantic parsing

Learn from data:

Input:

What’s California’s capital? Capital(California)

How long is the Mississippi river? RiverLength(Mississippi)

... ...

[Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; ...]

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Learn from data:

Input:



... ...

Output:

countries in asia ⇒ Type.Country u ContainedBy.Asia


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Learn from data:

Input:



... ...

Output:

countries in asia ⇒ Type.Country u ContainedBy.Asia

What are the problems with this form of supervision?


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Breadth

Depth

information retrieval

semantic parsing

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Outline


– Applications

– Background

– Short history

– Goals and plan


– Worlds


– Lambda-DCS


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Goals

Be able to go and build a semantic parser

Learn about modern techniques and challenges in semantic parsing

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Plan

Class 1: Representation

• Lambda calculus

• Lambda DCS

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Plan

Class 1: Representation

• Lambda calculus

• Lambda DCS

Class 2: Compositionality

• Grammars

• Lexicons

• Derivations

• Modelling

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Plan

Class 3: Learning

• Traning from logical forms

• Traning from denotations

• Additional training signals

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Plan

Class 3: Learning

• Traning from logical forms

• Traning from denotations

• Additional training signals

Class 4: Search

• CKY

• Beam-search

• Agenda-based parsing

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Plan

Class 5: Advanced topics

• Intermediate representations

• Building semantic parsers overnight

• Deep learning

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About me

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Outline


– Applications

– Background

– Short history

– Goals and plan


– Worlds


– Lambda-DCS


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Expressing language with logical form

How to handle various linguistic phenomena

• entities, relations

• Multi-arg events

• Comparatives, superlatives

• Quantifiers, determiners

Goal: Intuition for how to write down logical forms

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Outline


– Applications

– Background

– Short history

– Goals and plan


– Worlds


– Lambda-DCS


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Worlds

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Worlds



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Worlds




Send a reminder to students that did not submit homework

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Worlds




Send a reminder to students that did not submit homework

World determines relevant language

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Worlds

• Query a DB

Mountains in Italy

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Worlds

• Query a DB

Mountains in Italy

• Update a DB

Add a meeting from 8am-10am Monday to Friday

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Worlds

• Query a DB

Mountains in Italy

• Update a DB

Add a meeting from 8am-10am Monday to Friday

• Execute in an environment

Bring me the cup on the table

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Querying a DB

Type

BarackObama Person

MichelleObama Person

Honolulu CityTown

Hawaii USState

ContainedBy

Honolulu Hawaii

Honolulu UnitedStates

Chicago Illinois

Chicago UnitedStates

Profession

BarackObama Politician

BarackObama Author

MichelleObama Lawyer

PlaceOfBirth

BarackObama Honolulu

MichelleObama Chicago

Marriage

BarackObama Event8

MichelleObama Event8

GreaterThan

2 1

8 3

... ...

startDate

Event8 1992.10.03

Event13 2001.13.02

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Graph DB

BarackObama

Person

Type

Politician

Profession

1961.08.04

DateOfBirth

HonoluluPlaceOfBirth

Hawaii

ContainedBy

City

Type

UnitedStates

ContainedBy

USState

Type

Event8

Marriage

MichelleObama

Spouse

Type

FemaleGender

1992.10.03

StartDate

Event3

PlacesLived

Chicago

Location

Event21

PlacesLived

Location

ContainedBy

Subset of Freebase (2B facts)

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Representations

Simply-typed lambda calculus (Carpenter, 1998)

• General formalism for natural language semantics

Lambda-DCS (Liang, 2013)

• Natural language interface for databases

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Outline


– Applications

– Background

– Short history

– Goals and plan


– Worlds


– Lambda-DCS


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Simply-typed lambda calculus

• Constants

• Variables

• Lambda terms

• Function application

adapted from Yoav Artzi

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Each type has a set of constants and variables

Constants

Chicagoe

Event8ev

City〈e,t〉

PlaceOfBirthOf〈e〈e,t〉〉

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Each type has a set of constants and variables

Constants

Chicagoe

Event8ev

City〈e,t〉

PlaceOfBirthOf〈e〈e,t〉〉

Variables

xe, ye

aev, bev

f〈e〈e,t〉〉, g〈e〈e,t〉〉

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Function application

t: (PlaceOfBirthOf(BarackObama))Chicago)

t: City(Honolulu)

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Function application

t: (PlaceOfBirthOf(BarackObama))Chicago)

t: City(Honolulu)

Lambda terms

〈e, t〉: λx.President〈e,t〉(xe)

〈e, t〉: λx.President(x) ∧ Lawyer(x)

〈e〈e, t〉〉: λx.λyPlaceOfBirthOf(x, y)

Logical operators are defined as higher-order constants

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Function application and beta reduction

t : λx.City(x)(Honolulu)

beta

t : City(Honolulu)

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Function application and beta reduction

t : λx.City(x)(Honolulu)

beta

t : City(Honolulu)

t : λx.(λy.PlaceOfBirthOf(x, y)(BarackObama))(Chicago)

beta

t : PlaceOfBirthOf(BarackObama,Chicago)

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Outline


– Applications

– Background

– Short history

– Goals and plan


– Worlds


– Lambda-DCS


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Lambda-DCS

• Can be viewed as syntactic sugar for lambda-calculus

• Concise logical forms that mirror natural langauge

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Logical forms: entity

proper nouns

lambda-dcs Chicago

lambda calculus λx.x = Chicago

denotation {Chicago}graph template Chicago

[Liang, 2013]

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Logical forms: join

Verbs and prepositions

HasBirthplace.Chicago

λx.HasBirthplace(x,Chicago)

{MichelleObama}o ChicagoHasBirthplace

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Logical forms: join





ChildOf.HasBirthplace.Chicago

λx.∃yChildOf(x, y) ∧HasBirthplace(y,Chicago)

{NatashaObama, MaliaObama}o yChildOf ChicagoHasBirthplace

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Logical forms: join





ChildOf.HasBirthplace.Chicago

λx.∃yChildOf(x, y) ∧HasBirthplace(y,Chicago)

{NatashaObama, MaliaObama}o yChildOf ChicagoHasBirthplace

BirthDateOf.ChildOf.HasBirthplace.Chicago

λx.∃y∃zBirthDateOf(x, y) ∧ChildOf(y, z) ∧HasBirthplace(z,Chicago)

{06.10.2001, 07.04.1998}o yBirthdateOf zChildOf ChicagoHasBirthplace

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Logical forms: intersection, union, negation

Profession.Lawyer uHasBirthplace.Chicago

λx.Profession(x,Lawyer) ∧HasBirthplace(x,Chicago)

{MichelleObama}o

Lawyer

Profession

Chicago

HasBirthplace

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{MichelleObama}o

Lawyer

Profession

Chicago

HasBirthplace

Profession.Lawyer tHasBirthplace.Chicago

λx.Profession(x,Lawyer) ∨HasBirthplace(x,Chicago)

{MichelleObama,BarackObama}o

Lawyer

Profession

o

Chicago

HasBirthplace

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{MichelleObama}o

Lawyer

Profession

Chicago

HasBirthplace

Profession.Lawyer tHasBirthplace.Chicago

λx.Profession(x,Lawyer) ∨HasBirthplace(x,Chicago)

{MichelleObama,BarackObama}o

Lawyer

Profession

o

Chicago

HasBirthplace

¬Profession.Lawyer

λx.¬Profession(x,Lawyer)

{MaliaObama,NatashaObama}49

Logical forms: n-ary relations

Where did Barack Obama live in 1985?

Where did Barack Obama live?

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Do we need a ternary predicate?

λx.PlaceLivedBy(x,BarackObama)

λx.PlaceLivedBy(x,BarackObama, 1985)

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Do we need a ternary predicate?

λx.PlaceLivedBy(x,BarackObama)

λx.PlaceLivedBy(x,BarackObama, 1985)

Neo-Davidsonian semantics: Event node modified by several arguments

o Event8LivedLocation BarackObamaPersonLiving

1985

StartLiving

LivedLocation.(PersonLiving.BarackObama u StartLiving.1985)

λx.∃e.LivedLocation(x, e) ∧PersonLiving(e,BarackObama) ∧ StartLiving(e, 1985)

{Chicago} 50

Logical forms: higher order functions

Operate on sets of entities (not entities)

count(Type.USState)

count(λx.Type(x,USState))

{50}

Similar for min, max, sum

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Logical forms: higher order functions

Operate on sets of entities (not entities)

count(Type.USState)

count(λx.Type(x,USState))

{50}

Similar for min, max, sum

argmax(Type.USState,Area)

argmax(λx.Type(x,USState), λx.λy.Area(x, y))

{Alaska}

Similar for argmin

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Logical forms: anaphora

those whose children influenced Barack Obama

HasChild.HasInfluence.BarackObama

λx.∃y.HasChild(x, y) ∧HasInfluence(y,BarackObama)

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Logical forms: anaphora

those whose children influenced Barack Obama

HasChild.HasInfluence.BarackObama

λx.∃y.HasChild(x, y) ∧HasInfluence(y,BarackObama)

those whose child influenced them

µx.HasChild.HasInfluence.x

λx.∃y.HasChild(x, y) ∧HasInfluence(y, x)

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Logical forms: lambda abstraction

How to construct compositional binary relations?

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Person who has the most children

argmax(Type.Person,Rev[λx.count(Rev[HasChildren].x)])

argmax(λx.Type(x,Person), λx.count(λy.HasChildren(x, y)))

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Person who has the most children

argmax(Type.Person,Rev[λx.count(Rev[HasChildren].x)])

argmax(λx.Type(x,Person), λx.count(λy.HasChildren(x, y)))

In some cases lambda calculus is more transparent

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Some things we skip

Determiners and quantification

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Dynamic environments

Database updates

Add a meeting with Tom from 2 to 4pm

Add(Type.Meeting uAttendee.Tom u StartTime.1400 uEndTime.1600)

[Artzi and Zettlemoyer, 2013]

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Dynamic environments

Database updates

Add a meeting with Tom from 2 to 4pm

Add(Type.Meeting uAttendee.Tom u StartTime.1400 uEndTime.1600)

Executing instruction

[Artzi and Zettlemoyer, 2013]

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Outline


– Applications

– Background

– Short history

– Goals and plan


– Worlds


– Lambda-DCS


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Exercise

http://esslli2016.unibz.it/?page id=356

or

cd /usr/local/sempre

./run @mode=simple-lambdadcs

(loadgraph esslli 2016/geo880.kg)

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Exercise

a.b (a b)

a u b (and a b)

rank-m top-n for unary and binary (argmax m n u b)

count (count u)

numbers (number n)

µx.u (mark x u)

Rev(u) (rev u)

Type type.object.type

USState location.us state

City location.citytown

MajorCity location.major citytown

Area location.location.area

Size location.location.size

Population location.location.population

Capital location.us state.capital

Border location.us state.border

Traverse location.river.traverse

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Exercise

Write logical forms for the following utterances:

state with the largest area

Top 5 cities by area

Major cities with at least 140,000 inhabitants

How many states have capitals with at least 200,000 inhabitants

State bordering Oregon and Washington

Second highest mountain in California

Which states’ capitals are also their largest cities by population

State that is traversed by the most rivers

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Solution

(argmax 1 1 (type.object.type location.us state) location.location.area)

(argmax 1 5 (type.object.type location.citytown) location.location.size)

(and (type.object.type location.major citytown) (loca-tion.location.population (> (number 140000))))

(count (location.us state.capital (and (type.object.type location.capital)(location.location.population (> (number 200000))))))

(and (type.object.type location.us state) (and (location.us state.borderstate.oregon) (location.us state.border state.washington)))

(argmax 2 1 (and (type.object.type location.mountain) (loca-tion.location.containedby state.california)) location.location.elevation)

(and (type.object.type location.us state) (mark x (loca-tion.us state.capital (argmax 1 1 (location.location.containedby(var x)) location.location.population))))

(argmax 1 1 (type.object.type location.us state) (reverse (lambda x(count (location.river.traverse (var x))))))

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Summary

• Key technology for conversational interfaces

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Summary


• Expressing language constructs with logical forms

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Summary


• Expressing language constructs with logical forms

• But how do we construct logical forms automatically?

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Executable Semantic Parsing - Tel Aviv Universityjoberant/teaching/class1_index.pdf · SHRDLU...

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Transcript of Executable Semantic Parsing - Tel Aviv Universityjoberant/teaching/class1_index.pdf · SHRDLU...