Generation of Referring Expressions: the State of the Art SELLC Summer School, Harbin 2010

Kees van Deemter

Computing Science

University of Aberdeen

Introductory remarks about the course

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)

Who are you? Substantial background in

logic/maths? linguistics? computation? philosophy? other?

Level of education: studying for your Masters degree PhD degree other?

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)

Some simple examples

Assume that nothing has ever been said Your task is to refer to an object ...

Example Situation

a, £100 b, £150

c, £100 d, £150 e, £?Swedish Italian

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

Game

1. Describe object a.

2. Describe object d.

3. Describe object e.

Game

1. Describe object a: {desk,sweden}, {grey}

2. Describe object d: {chair, 150}

3. Describe object e: {chair, neither 100 nor 150}

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?

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 ...

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).

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

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)

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

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)

Motivation/assumptions

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

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!

Time to move on ...

... to a brief overview of NLG