CS计算机代考程序代写 prolog assembly interpreter 7b_Semantics_Pragmatics.dvi

7b_Semantics_Pragmatics.dvi

COMP9414 Semantics and Pragmatics 1

This Lecture

� Semantics

◮ Features and Augmented Grammars

◮ Semantic Interpretation

� Pragmatics

◮ Discourse Structure

◮ Speech Act Theory

◮ Dialogue Management

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COMP9414: Artificial Intelligence

Lecture 7b: Semantics and Pragmatics

Wayne Wobcke

e-mail:w. .au

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Noun Features

Pronoun Person Number Gender Case

I first sing nom

you second sing/plural nom/acc

we first plural nom

us first plural acc

he third sing masculine nom

she third sing feminine nom

it third sing neuter nom/acc

him third sing masculine acc

her third sing feminine acc

they third plural nom

them third plural acc

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Agreement

� Number agreement

◮ Which country borders France?

◮ Which countries border France?

◮
∗Which country border France?

◮
∗Which countries borders France?

� Case

◮ I saw him

◮
∗Him saw I

� To capture these facts, need lexical knowledge

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Verb Forms

Progressive Verb sequence Example

present is + present participle He is crying

past was + present participle He was crying

future will + be + past participle He will be crying

present perfect has + been + pres participle He has been crying

future perfect will + have + been + past participle He will have been crying

past perfect had + been + pres participle He had been crying

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Verb Forms

Verb Form Example

cry base

cries simple present He cries

cried simple past He cried

crying present participle He is crying

cried past participle He has cried

Tense Verb sequence Example

future will + infinitive He will cry

present perfect has + past participle He has cried

future perfect will + have + past participle He will have cried

past perfect had + past participle He had cried

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Augmented Context Free Grammars

� Each symbol has a collection of features

� Grammar rules constrain feature values

◮ Use unification to enforce constraints, as in Prolog

� Features (mainly) derived from lexical items

� Some also from grammar rules (e.g. Case)

� Simple example

◮ S(number: N) → NP(number: N) VP(number: N)

◮ Enforce number agreement by unification (matching)

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Subcategorization

[] Jack laughed

[NP] Jack found a key

[NP, NP] Jack gave Sue the paper

[VP(inf)] Jack wants to fly

[NP, VP(inf)] Jack told the man to go

[VP(ing)] Jack keeps hoping for the best

[NP, VP(ing)] Jack caught Sam looking at his desk

[NP, VP(base)] Jack watched Sam look at his desk

Determines mandatory sentence constituents

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Example

Note: Not all arguments are specified on tree nodes

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Typical (Small) Grammar

S(Agr) → NP(Agr), VP(VForm, Agr)

NP(Agr) → Det(Agr), N(Agr)

NP(Agr) → PRO(Agr)

VP(VForm, Agr) → V(VForm, Agr, []) # subcat feature

VP(VForm, Agr) → V(VForm, Agr, [NP]), NP(Agr)

VP(VForm, Agr) → V(VForm, Agr, [VP(inf)]), VP(inf, )

VP(VForm, Agr) → V(VForm, Agr, [ADJP]), ADJP

VP(inf, Agr) → to, VP(base, Agr)

ADJP → ADJ([])

ADJP → ADJ([VP(inf)], VP(inf, )

Convention is to unify (match) arguments in rule with same name

Note: is a variable that matches anything

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Thematic Roles

� Agent (intentional actor)

� Object/Theme (object on which action performed)

� Patient (animate object affected psychologically)

� Co-agent

� Instrument

� Beneficiary

� Location

� Source

� Destination

Often hard to distinguish!

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Semantic Interpretation

� Logical form (LF) captures underlying “meaning”

◮ Depends on purpose – no one “true” meaning

� Logical form should resolve semantic ambiguity

� Compute LF of sentence from LF of constituents

� Treat logical form as another feature

� Example: John sold a car to Mary

event(e, Sell) ∧ occur(e, past) ∧ agent(e, John) ∧ co-agent(e, Mary)

∧ object(e, {c: car})

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Logical Form (Chat-80)

� Logical Form (LF) is just another feature

◮ Formulae of the form XˆF where X is a variable and F is a formula

◮ Read “the X such that F”

� May need more than matching to compute logical forms

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Assigning Thematic Roles

� (Semantic) selection restrictions given by verb

◮ e.g. agent of ‘break’ is animate

� Prepositions indicate likely role

◮ e.g. ‘with’ implies instrument or co-agent

◮ e.g. ‘by’ implies location or agent

� Problem examples

◮ The window broke

◮ My car drinks petrol

Simple method but doesn’t always work ⇒ probabilities

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Example Grammar

S(Xˆ(VPForm ∧ NPForm)) → NP(XˆNPForm), VP(XˆVPForm)

NP(Form) → N(Form)

NP(Xˆ(PPForm ∧ NForm)) → Det, N(YˆXˆNForm), PP(YˆPPForm)

N(Xˆtrue) → what

N(Xˆ(X = france)) → france

N(XˆYˆ(capital(X,Y))) → capital

VP(Form) → V(be), NP(Form)

PP(Form) → P(of), NP(Form)

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Example Logical Form

What is the capital of France?

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Pragmatics

� Discourse Processing

◮ Reference Resolution

◮ Discourse Structure

� Speech as Rational Action

◮ Speech Act Theory

◮ Spoken Dialogue Systems

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Summary

� Disambiguation is central problem in NLP

� Use logical form language to resolve semantic ambiguity

� Augmented grammars can capture agreement and logical form

◮ Focus on lexical knowledge

� No one “right” logical form language

◮ Case frames, First-order logic, · · ·

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Reference Resolution

We bought a desk.

The drawer was broken.

Reserve a flight to Brisbane for me.

Reserve one for Norman too.

John gave Mary five dollars.

It was more than he gave Sue. One of them was counterfeit.

Each girl took a handout.

Then she threw it away.

John didn’t marry a Swedish blonde.

She was Danish/She had brown hair/She’s living with him.

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Reference Resolution

Jack lost his wallet in his car.

He looked for it for several hours.

Jack forgot his wallet.

Sam did too.

Jack forgot his wallet.

He looked for someone to borrow money from.

Sam did too.

I found a red pen and a pencil.

The pen didn’t work.

I saw two bears.

Bill saw some too.

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Focus Hypothesis

� At any time, there is a discourse entity that is the preferred antecedent

for pronouns in the current local context – the discourse focus

1. If any object in the local context is referred to by a pronoun in the

current sentence, then the focus of the current sentence must also

be pronominalized

2. The focus is the most preferred discourse entity in the local

context that is referred to by a pronoun

3. Maintaining the focus is preferred to changing the focus

� Order possible antecedents subject > object > rest

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Discourse Entities

� The possible antecedents of pronouns

◮ Noun phrases explicitly mentioned in recent discourse

◮ A set of (implied) discourse entities

• e.g. the handouts each girl took, the set of girls

◮ An object related to (evoked by) a discourse entity

• e.g. the drawer of the desk

◮ Fillers of roles in stereotypical scenarios

• e.g. waiters in restaurants

� Assume discourse is divided into “local contexts”

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Discourse Structure

E: So you have the engine assembly finished.

Now attach the rope to the top of the engine.

By the way, did you buy petrol today?

A: Yes. I got some when I bought the new lawnmower wheel.

I forgot to take my can with me, so I bought a new one.

E: Did it cost much?

A: No, and I could use another anyway.

E: OK. Have you got it attached yet?

Tracking focus isn’t enough

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Example

Jack left for the party late. (focus = Jack)

When he arrived, Sam met him at the door. (focus = he/Jack)

He decided to leave early. (focus = he/Jack)

Jack saw him in the park. (focus = him)

He was riding a bike. (focus = he/him)

While Jack was walking in the park, he met Sam. (focus = he/Jack)

He invited him to the party. (focus = Jack or Sam)

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Hierarchical Structure

SEG1
Jack and Sue went to buy a new lawnmower since their old one

was stolen.

SEG2
Sue had seen the man who took it and she had chased

them down the street, but they’d driven away in a

truck.

After looking in the store, they realized they couldn’t afford one.

SEG3
By the way, Jack lost his job last month so he’s been

short of cash recently. He has been looking for a new

one, but so far hasn’t had any luck.

Anyway, they finally found a used one at a garage sale.

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Discourse Segments

� Recency-based technique for reference resolution

� Fixed time and location or simple progression

� Fixed set of speakers/hearers

� Fixed set of background assumptions

� Intentional view

◮ Segment elements contribute to same discourse purpose

� Informational view

◮ Segment elements are related temporally, causally, etc.

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Managing the Attentional Stack

� Extending a segment

◮ All references can be resolved in current segment

◮ Same tense or same tense without perfect aspect

� Creating a new segment

◮ Change in tense (progression of discourse)

◮ Cue phrase indicating digression

� Closing a segment

◮ Discourse purpose of new segment part of immediate parent

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Attentional Stack

� Stack corresponding to segment hierarchy at point in time

◮ e.g. [SEG1, SEG2] or [SEG1, SEG3]

� Stack update on starting SEG3

◮ Either push new segment

• giving [SEG1, SEG2, SEG3]

◮ Or close current segment and push new segment

• giving [SEG1, SEG3]

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Actions Involved in Speech Acts

� Locutionary act

◮ Physical act of saying something

� Illocutionary act

◮ Speech act performed in making utterance

� Perlocutionary act

◮ Effect on hearers’ actions thoughts, beliefs, etc.

Communication involves intention recognition

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Speech Acts

� Speech as goal-directed rational activity

◮ e.g. promise, threaten, warn, order, advise, state request, inform,

assert, deny, apologize, thank, greet, criticize, dare, hope,

congratulate, welcome, bless, curse, toast, challenge, announce,

declare, question, . . .

� Sometimes explicit in utterances, use of so-called performative verbs

Speech act type is utterance’s illocutionary force

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Indirect Speech Acts

� Use of one kind of illocutionary act to perform another

◮ Can you pass the salt?

◮ Do you know the time?

◮ You are standing on my foot

◮ Why don’t you leave now?

◮ Would you like a game of tennis?

◮ If I were you, I’d sell that car

◮ Now would you mind getting off my foot!

� Even harder to recognize speaker’s intention

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Characterizing Illocutionary Acts

request warn

propositional

content

Future act A of H Future event or state E

preparatory

conditions

H able to do A

S believes H able to do A

Not obvious to both S, H

that H will do A anyway

S has reason to believe E will oc-

cur and is not in H’s interest

Not obvious to both S, H that E

will occur

sincerity con-

ditions

S wants H to do A S believes E not in H’s best inter-

est

essential con-

ditons

Counts as an attempt to

get H to do A

Counts as undertaking to the ef-

fect that E not in H’s best interest

Heavily oriented towards speaker’s intentions

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Initiative

� System Initiative

◮ System “controls” dialogue by prompting user for information

◮ Useful for specific tasks

• Booking flights, ordering pizza, placing bets

� User Initiative

◮ User “controls” dialogue by questions, commands

◮ Useful for simple tasks, e.g. web search, training and simulation

� Mixed Initiative

◮ Mixture of system initiative and user initiative

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Spoken Dialogue Systems

� Feasible now with good speech recognition

◮ Speaker dependent or domain specific

� Based on limiting possible dialogue structure

◮ Frames with slots that need filling

◮ Graphs representing possible transitions

◮ Rules for defining actions based on prior context

◮ Limited range of subdialogues (e.g. clarification)

Aim is to perform as little reasoning as possible

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System Architecture

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Smart Personal Assistant

� Integrated collection of personal (task) assistants

� Each assistant specializes in a task domain

◮ E-mail and calendar management

� Users interact through a range of devices

◮ PDAs, desktops, iPhone?

� Focus on usability

◮ Multi-modal natural language dialogue

◮ Adapt to user’s device, context, preferences

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BDI Agent Architecture

� Beliefs, desires, intentions explicit

◮ Predefined plans for achieving goals

� Interpreter cycle – PRS (Procedural Reasoning System)

◮ Event-driven selection and execution of plans

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Partial Parsing

� Full parsing is inappropriate

◮ Limited accuracy of speech recognition

◮ Regular use of short-form expressions

◮ Unconstrained language vocabulary

• e.g. “Are there any new messages from . . . ”

� Shallow syntactic frame

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Dialogue Manager Plans

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Dialogue Manager Beliefs

� Dialogue model

◮ Discourse history (stack of conversational acts)

◮ Salient list (ranked list of recently mentioned objects)

� Domain knowledge

◮ Supported tasks (for each task assistant)

◮ Domain-specific vocabularies for task interpretation

� User model

◮ User context information (device, modalities, . . .)

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Summary

� Dialogue systems are current “hot” topic

� Feasible because of high-quality speech recognition

◮ Questions over accuracy, usability of Siri, Alexa, etc.

� Industry impetus is automation of routine interactions to reduce costs

◮ Lot of hype compared to actual deployed systems

� Simple dialogue management techniques include graphs of dialogue

actions and rules based on dialogue context

◮ Current possible interactions not very sophisticated

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