Linguistic issues in building
dialogue systems
Radhika Mamidi
IIIT-H
Outline
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Linguistic issues in NLP including Pragmatics
Computational Pragmatics
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Pragmatics
Discourse Analysis
Conversation Analysis
Spoken Dialogue Systems
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Types, models, domains
Comparing human-human vs human-system
dialogues
Speech Act interpretation
Why is Natural Language Processing so difficult?
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Human language is:
 Complex and Ambiguous
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We use language creatively
 We don’t mean what we say!
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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
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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!
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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:
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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
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A man like him is hard to find.
[For a chronically absent employee]
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He's an unbelievable worker.
[For a dishonest employee]
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You would indeed be fortunate to get this person to
work for you.
[For a lazy employee]
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There is nothing you can teach a man like him.
[For a stupid employee]
Cooperative model: various types of
knowledge
Eg: The building blocks…
(Greene, 1986)
Pragmatics
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Study of how utterances have meanings in situations.
(Leech, 1983)
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Study of how more gets communicated than is said.
(Yule, 1996)
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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
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the ability to comprehend and produce a
communicative act
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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
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deals with relations between linguistic aspects and
aspects of context.
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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”
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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
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Turn Constructional Component
Turn Allocational Component
Sequence Organization
Adjacency pairs: greeting-greeting, question-answer pairs
Pre-sequences
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Preference Organisation:
agreement and acceptance are promoted over their alternatives
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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?
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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
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Phone-based applications: timetable info or flightbooking
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 webbased 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
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Ultimate goal to have Dialog Systems that talk like human
beings and display intelligence in understanding the
complex cognitive structure of language.
Dialogue domains
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Travel information (SUNDIAL, ATIS)
Transport (TRAINS)
Business Appointments (VERBMOBIL)
Car-Navigation
Access to on-line information
(SUN Speech Acts)
Dialogue models
Dialogues can be:
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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).
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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
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Single initiative system
Guide user through a series of scripted prompts.
Eg. Telephone enquiry
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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…
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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 computerhuman dialogues
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Human-Human
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Hums, grunts, pauses, false starts, hesitations
Barge in conversations
Elliptical constructions
Context is important
Computer-Human dialogues
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Rigid; turn by turn pattern
User speaks a word or two
Intelligent agent components
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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):
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Assertives
Directives
Commissives
Expressives
Declarations
Challenges
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Speech recognition errors
Parsing language in practical dialogue
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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…
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Recognise intention of speaker
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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
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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…
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Ellipsis
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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
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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
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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
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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
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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
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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 multiparty interactions.
References
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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
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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.