Linguistic issues in building

bouncerarcheryAI and Robotics

Nov 14, 2013 (4 years and 6 months ago)


Linguistic issues in building

dialogue systems

Radhika Mamidi



Linguistic issues in NLP including Pragmatics

Computational Pragmatics


Discourse Analysis

Conversation Analysis

Spoken Dialogue Systems

Types, models, domains

Comparing human
human vs human

Speech Act interpretation

Why is Natural Language Processing so difficult?

Human language is:

Complex and Ambiguous

We use language creatively

We don’t mean what we say!

Language Understanding needs contextual and general
knowledge apart from linguistic knowledge.

To know what we mean shared knowledge is

Representing all this knowledge computationally is THE

Let’s analyze this spoken sentence:

“I made her duck”

How many meanings/interpretations?

Human language is complex and ambiguous

When shot at, the dove dove into the bushes.

The insurance was invalid for the invalid.

They were too close to the door to close it.

The buck does funny things when the does
are present.

There was a row among the oarsmen about
how to row.

Upon seeing the tear in the painting I shed a

Language understanding: Parsing problem!

Gene Autry is better after being kicked by a

The women included their husbands and their
children in their potluck suppers.

Two cars were reported stolen by the
Groveton police yesterday.

(Steven Pinker. 1994. The language instinct. Morrow.

We use language creatively…

Example recommendations:

A man like him is hard to find.

He's an unbelievable worker.

You would indeed be fortunate to get this person to
work for you.

There is nothing you can teach a man like him.

I can assure you that no person would be better for
the job.

What we say and what we mean

A man like him is hard to find.

[For a chronically absent employee]

He's an unbelievable worker.

[For a dishonest employee]

You would indeed be fortunate to get this person to
work for you.

[For a lazy employee]

There is nothing you can teach a man like him.

[For a stupid employee]

Cooperative model: various types of

(Greene, 1986)

Eg: The building


Study of how utterances have meanings in situations.

(Leech, 1983)

Study of how more gets communicated than is said.

(Yule, 1996)

How people comprehend and produce a
communicative act or speech act in a concrete speech

It distinguishes two intents or meanings in each
utterance or communicative act of verbal

Informative intent = the sentence meaning

Communicative intent = speaker meaning

(Sperber and Wilson, 1995).

Pragmatic competence

the ability to comprehend and produce a
communicative act

Includes one's knowledge about the social
distance, social status between the speakers
involved, the cultural knowledge such as
politeness, and the linguistic knowledge
explicit and implicit.

Topics in Pragmatics

deals with relations between linguistic aspects and
aspects of context.

Conversational Implicature

A: Coffee?

B: It will keep me awake.


“I bought this book in Italy last summer”

Speech Acts

“Why don’t you call Mary?”


“I’d like you to leave that over there and come here now”

Discourse Analysis

Anaphora resolution

John and Mary bought new cars. They are good friends.

John and Mary bought new cars. They are 2008 models.

Rhetorical relations

John fell. Jack pushed.

John went to work. He works at IBM.

John went to work. He took a taxi.


Mary bought a new car. So did Susan.

Mary bought a new dress. So did Susan.

Conversation Analysis

Turn Constructional Component

Turn Allocational Component

Sequence Organization

Adjacency pairs: greeting
greeting, question
answer pairs


Preference Organisation:

agreement and acceptance are promoted over their alternatives


who initiates repair (self or other) and by who resolves the problem
(self or other)

Sacks, H., Schegloff, E. A., & Jefferson, G. (1974)

Computational Pragmatics

“Computational pragmatics studies, from an explicitly computational
point of view, how relations between
linguistic phenomena and
their context of use

govern speakers’ abilities to interpret and
generate utterances in conversation”

How to compute these relations in terms of explicit

representations. . .

given a linguistic expressions, how to compute the relevant
contextual properties

given a particular context, how to compute the relevant

linguistic expression

(Bunt & Black, 2000)

Application of computational pragmatics

Work on computational pragmatics often takes
place within research on

dialogue systems.

Systems that are able to interact with human
users in natural language.

Helps us make decisions on how to deal in a
computational way

with all phenomena
related to
language use

What is a dialogue system?

An artificial agent like robot or a computer
system that can interact with human beings.

Helps us understand the nature of dialogue
and test theories

Helps us understanding the collaborative
nature of interaction

Helps us access information and services
more efficiently

Uses of dialogue systems

based applications: timetable info or flight

Personal assistant: understand user needs and

Intelligent tutoring: student engagement

Embodied conversational agents

Engagement via realistic and affective
physical and facial gestures

Intelligent environments: home or car

Understanding user situation and activity


(Mamidi and Khan, 2005)

Available intelligent dialogue systems

Interactive Voice Systems [e.g. railway enquiry system
asking the user to press certain numbers for
accomplishing the task]

Answering Systems [e.g. START, a web
based QA system answering user's questions on
movies, places, people etc.]

Natural Language Interfaces [e.g. tutoring systems,
trip planning systems]


Restricted Domain

Easier to implement

Ultimate goal to have Dialog Systems that talk like human
beings and display intelligence in understanding the
complex cognitive structure of language.

Dialogue domains

Travel information (SUNDIAL, ATIS)

Transport (TRAINS)

Business Appointments (VERBMOBIL)


Access to on
line information

(SUN Speech Acts)

Dialogue models

Dialogues can be:

mediated: ordinary every
day human

dialogue analysed by computational means (dialogue data

is recorded, transcribed and analysed to build automated


mediated: The computer offers assistance to the
participants (VERBMOBIL).

Simulated: Both participants are human, but one pretends
to be a computer system.

simulated: Genuine interaction between human and

computer (fully
fledged Dialogue Systems)

Types of dialog systems

Single initiative system

Guide user through a series of scripted prompts.

Eg. Telephone enquiry

Mixed initiative system

Cooperative and flexible responses to user queries.

Eg. Restaurant database

Sample dialogue: CSLI Dialogue System
for in
car devices

What music do you have?

S: I have rock, soul …
// aggregation

What rock songs?

Sys: Just a second: you have to turn left at the next intersection
// interruption


S: I have songs by U2, Cake, Green Day, Jet, …
// return to previous thread

Play something by them
// pronominal reference

S: OK. Playing xxx.
< plays random song by Jet >

Play American Idiot instead.

S: Do you mean the song or the album?
// disambiguation

The album please
// fragment

I need somewhere for lunch

S: There is a highly rated vegetarian restaurant near here.
// knows preferences

What other cuisines

S: There are Italian, Japanese, Malaysian, Nepali, and Thai restaurants close


A: And what time would you like to leave
Pittsburgh? (relevant next contribution)

C: Uh hmm, I don’t think there’s many options
for non

A: Right. There’s three non
stops today.

Comparing human
human vs computer
human dialogues


Hums, grunts, pauses, false starts, hesitations

Barge in conversations

Elliptical constructions

Context is important

Human dialogues

Rigid; turn by turn pattern

User speaks a word or two

Intelligent agent components



the agent must be able to perceive the world
around it



the agent must have a representation of the present
state of the world



the agent should have positive or negative
responses to various states of the world, creating a way to
compare the desirability of states



the agent must be able to reason about
ways to attain other states



the agent must be able to decide to act to get to a
different state



the agent must be able to maintain the course of
action decided on



the agent must be able to act and thus change its state


Illocutionary speech acts

Searle (1975):







Speech recognition errors

Parsing language in practical dialogue

Need to capture what was said

Spoken language is not sentence based

A single utterance realises a sequences of speech act.

Intention recognition

Mixed initiative

Integrate dialogue and task performance

dependent interpretation

Dialogue strategies (turn
taking mechanisms)

If computers were to speak like us…

Recognise intention of speaker

A1: Lend me your umbrella.
It is cloudy
. [Request]

A2: Don't water the plants now.
It is cloudy
. [Warning]

A3: It will rain today.
It is cloudy
. [Assertion]

A4: I hope the pictures will come out well.
It is cloudy

Make proper inference

B1: Did you look at the sentence I sent you to translate.

C1: Yeah. It was such an easy sentence!

B2: Was it easy?

C2: No, I meant it was tough.



Retaining the logical form of previous sentence.

Reconstructing full content.

Turn management: determining when the
turn is over and who talks next


acknowledgement, repetition

Clarification: question to resolve some lack of

Anaphora resolution

Speech act interpretation

BDI model

Cue based model

Belief Desire Intention (BDI) model

Bunt and Black (2000) define this line of inquiry as

to apply the principles of rational agenthood to the
modeling of a (computer
based) dialogue
participant, where
a rational communicative agent is
endowed not only with certain private knowledge
and the logic of belief
, but is considered to also
assume a great deal of common knowledge/beliefs
with an interlocutor
, and to be able
to update beliefs
about the interlocutor’s intentions and beliefs as a
dialogue progresses.

Belief Desire Intention algorithm

Extremely powerful approach to dialogue act
comprehension/speech act interpretation.

Uses rich knowledge structures and powerful
planning techniques.

Addresses even subtle indirect uses of
dialogue acts.

Incorporates knowledge about speaker and
hearer intentions, actions, knowledge, and
belief that is essential for any complete model
of dialogue.


It requires that each utterance have a single
literal meaning, which is operated on by plan
inference rules to produce a final non

Much recent work has argued against this
first non
second model of


Cue model

Listener uses different cues in the input to help
decide how to build an interpretation.

The surface input to the interpretive algorithm
provides clues to structure
building, rather than
providing a literal meaning which must be
modified by purely inferential processes.

What characterizes a cue
based model is the
use of different sources of knowledge (cues) for
detecting a speech act, such as lexical,
collocational, syntactic, prosodic, or
structure cues


Pragmatics is the base of Computational

Dialogue allows to explore novel challenges
in language technologies.

Understanding human
human dialogue helps
in building human
system dialogue.

Goal is to build robust dialogue systems for
initiative, multi
domains and multi
party interactions.


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CA: Benjamin Cummings.

Allen, James, Donna Byron, Myroslava Dzikovska, George
Ferguson, Lucian Galescu, and Amanda Stent. 2001. Towards
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building Dialog Systems. Presented at The Linguistic Society of
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