generation of referring expressions: the state of the art sellc summer school, harbin 2010 kees van...
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Generation of Referring Expressions: the State of the Art SELLC Summer School, Harbin 2010
Kees van Deemter
Computing Science
University of Aberdeen
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Introductory remarks about the course
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I am Kees van Deemter…
Reader in Computing Science, University of Aberdeen (2004-now)
Principal Research Fellow, ITRI, University of Brighton (1997-2004)
Research Scientist, Philips Electronics/IPO (1984-97) PhD University of Amsterdam 1991 Research interests:
Formal semantics of Natural Language (ambiguity, vagueness) Generation of text Multimodality (speech, graphics)
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Who are you? Substantial background in
logic/maths? linguistics? computation? philosophy? other?
Level of education: studying for your Masters degree PhD degree other?
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This course
An exploration into referring expressions, from the perspective of Natural Language Generation (NLG)
Generation of Referring Expressions (GRE) The key question:
How can we find the “best” referring expression in a given situation? (most effective, most fluent, ..)
The ideal answer to the question is an algorithm (i.e. a recipe for cooking up the best referring expression)
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Some simple examples
Assume that nothing has ever been said Your task is to refer to an object ...
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Example Situation
a, £100 b, £150
c, £100 d, £150 e, £?Swedish Italian
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Formalised
Type: furniture (abcde), desk (ab), chair (cde) Origin: Sweden (ac), Italy (bde) Colours: dark (ade), light (bc), brown (a) Price: 100 (ac), 150 (bd) , 250 ({}) Contains: wood ({}), metal ({abcde}), cotton(d)
Assumption: all this is mutual knowledge
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Game
1. Describe object a.
2. Describe object d.
3. Describe object e.
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Game
1. Describe object a: {desk,sweden}, {grey}
2. Describe object d: {chair, 150}
3. Describe object e: {chair, neither 100 nor 150}
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Questions
When is it a good idea to add “logically redundant” information to a referring expresion?
How to determine whether an algorithm is good? Reference serves to pick out an object (i.e., to
individuate it). What does it mean to offer a useful description of an object?
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Prerequisites
The most rudimentary understanding of computing will suffice
You need to be able to think in terms of sets and their associated operations. (Equivalently: propositions and Boolean operators)
Caveat: Some important issues will not be covered ...
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Earlier courses
ESSLLI 2002(?) LOT, Tilburg 2008 HIT, Harbin 2010
SELLC longer than HIT
(5 lectures / 2 lectures + project) updated from LOT
(adding Description Logic, vagueness, surface phenomena).
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Limitations of the course
Relational/recursive NPs will not be discussed in depth (Dale and Haddock 1991) “(the pen on (the table in (the corner)))”
Perhaps the most important omission is how discourse affects reference: Anaphora / salience will not play a large role
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Another perspective on the course
2003-2007: EPSRC project “Towards a Unified Algorithm for the Generation of Referring Expressions” (TUNA)
This course asks what we have learned from TUNA and its
aftermath (e.g., the TUNA-inspired evaluation challenges)
what’s the way ahead (new techniques, open questions, links with philosophy and psycholinguistics)
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Plan of the course
1. GRE and its place in Natural Language Generation
2. A seminal paper on GRE: Dale & Reiter (1995)
3. Testing Dale and Reiter’s claims
4. A choice of more specialized topics:1. reference to sets2. links with KR & Description Logic3. generating vague descriptions4. making things easy for the hearer
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Plan of the course
Reading material (See course web page):
Krahmer and Van Deemter [submitted] Computational Generation of Referring Expressions: a Survey. (Particularly sections 1,2,5)
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Motivation/assumptions
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Why study referring expressions?
Great practical relevance: even the simplest NLG systems have to do GRE
GRE is one of the best-understood tasks in NLG. Links with many areas of Cognitive Science and AI
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Hidden agenda:
Get more theoreticians interested in NLG more specifically, in the
generation of referring expressions
Ideas for new (PhD) projects are very welcome
Feel free to ask questions, at any time!
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Time to move on ...
... to a brief overview of NLG
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