Linguistic issues in building

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Linguistic issues in building

dialogue systems



Radhika Mamidi

IIIT
-
H

Outline


Linguistic issues in NLP including Pragmatics


Computational Pragmatics


Pragmatics


Discourse Analysis


Conversation Analysis


Spoken Dialogue Systems


Types, models, domains


Comparing human
-
human vs human
-
system
dialogues


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
necessary.


Representing all this knowledge computationally is THE
challenge.

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
tear.


Language understanding: Parsing problem!


Gene Autry is better after being kicked by a
horse.



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.
102.)

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
knowledge

(Greene, 1986)

Eg: The building
blocks


Pragmatics


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
situation.


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

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.


Presupposition



“I bought this book in Italy last summer”


Speech Acts



“Why don’t you call Mary?”


Deixis



“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.

Ellipsis



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


Pre
-
sequences



Preference Organisation:


agreement and acceptance are promoted over their alternatives



Repair:


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


Phone
-
based applications: timetable info or flight
-
booking


Personal assistant: understand user needs and
tasks


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

Architecture


(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]


Question
-
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]


Task
-
oriented


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)


Car
-
Navigation


Access to on
-
line information

(SUN Speech Acts)



Dialogue models

Dialogues can be:


Non
-
machine
-
mediated: ordinary every
-
day human

dialogue analysed by computational means (dialogue data

is recorded, transcribed and analysed to build automated

systems).


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



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


Non
-
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


U:
What music do you have?

S: I have rock, soul …
// aggregation

U:
What rock songs?

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

U:
OK
.

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

U:
Play something by them
.
// pronominal reference

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

U:
Play American Idiot instead.

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

U:
The album please
.
// fragment

U:
I need somewhere for lunch
.

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

U:
What other cuisines
?

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

Sample…


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
-
stop


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

Comparing human
-
human vs computer
-
human dialogues


Human
-
Human


Hums, grunts, pauses, false starts, hesitations


Barge in conversations


Elliptical constructions


Context is important


Computer
-
Human dialogues


Rigid; turn by turn pattern


User speaks a word or two

Intelligent agent components


perception

-

the agent must be able to perceive the world
around it


beliefs

-

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


desire/wants

-

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


planning/reasoning

-

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


commitment

-

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


intentions

-

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


acting

-

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










(Allen,1995)

Illocutionary speech acts

Searle (1975):


Assertives


Directives


Commissives


Expressives


Declarations

Challenges


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


Context
-
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
.
[Doubt]


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.

And…


Ellipsis


Retaining the logical form of previous sentence.


Reconstructing full content.


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


Grounding
-

acknowledgement, repetition


Clarification: question to resolve some lack of
understanding


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
follows:


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.

Drawback


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



Much recent work has argued against this
literal
-
first non
-
literal
-
second model of
interpretation.

Alternative
-

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
conversational
-
structure cues
.


Conclusion


Pragmatics is the base of Computational
Pragmatics.


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
mixed
-
initiative, multi
-
domains and multi
-
party interactions.



References


Allen, James. 1995. Natural Language Understanding. Menlo Park,
CA: Benjamin Cummings.


Allen, James, Donna Byron, Myroslava Dzikovska, George
Ferguson, Lucian Galescu, and Amanda Stent. 2001. Towards
Conversational Human
-
Computer Interaction. AI Magazine.


Allen, James, Donna Byron, Myroslava Dzikovska, George
Ferguson, Lucian Galescu, and Amanda Stent. 1998. Natural
Language Engineering. Cambridge University Press.


Bunt, Harry and William Black (eds.) 2000. Abduction, Belief and
Context in Dialogue. Amsterdam: John Benjamins.


Greene, Judith. 1986. Language Understanding: A cognitive
approach. Open university press.


Jurafsky, Daniel, and James H. Martin. 2000. Speech and Language
Processing: An Introduction to Natural Language Processing,
Computational Linguistics, and Speech Recognition. Prentice Hall.


Leech, Geoffrey N. 1983. Principles of Pragmatics. London:
Longman.

References



Levinson, Stephen C. 1983. Pragmatics. Cambridge University
Press.


Mamidi, Radhika and Monis Raja Khan. 2005. Linguistic issues in
building Dialog Systems. Presented at The Linguistic Society of
India Platinum Jubilee Conference, University of Hyderabad, India.
6
-
8 December, 2005


Ruslan, Mitkov (ed). 2003. The Oxford handbook of Computational
Linguistics. Oxford University Press.


Sacks, H, E. A. Schegloff, G Jefferson. 1974. A simplest
systematics for the organization of turn
-
taking for conversation.
Language,
50
, 696
-
735.


John Searle. 1975. Indirect speech acts. In
Syntax and
Semantics, 3: Speech Acts
, ed. P. Cole & J. L. Morgan, pp. 59

82. New York: Academic Press.


Sperber, D and D. Wilson. 1995. Relevance: Communication and
Cognition, 2nd ed. Oxford: Blackwell.


Yule, George.1996. Pragmatics (Oxford Introductions to Language
Study). Oxford University Press.