Generation of Referring Expressions: Managing Structural Ambiguities

Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 433–440 Manchester, August 2008 Generation of Referring Expressions: Managing Structural Ambiguities ∗ Imtiaz Hussain Khan and Kees van Deemter and Graeme Ritchie Department of Computing Science University of Aberdeen Aberdeen AB24 3UE, U.K. {i.h.khan,k.vdeemter,g.ritchie}@abdn.ac.uk Abstract Existing algorithms for the Generation of Referring Expressions tend to gen- erate distinguishing descriptions at the semantic level, disregarding the ways in which surface issues can affect their quality. This paper considers how these algorithms should deal with surface am- biguity, focussing on structural ambi- guity. We propose that not all ambigu- ity is worth avoiding, and suggest some ways forward that attempt to avoid un- wanted interpretations. We sketch the design of an algorithm motivated by our experimental findings. 1 Introduction A Noun Phrase (np) is a referring expression if its communicative purpose is to identify an object to a hearer. The Generation of Refer- ring Expressions (gre) is an integral part of most Natural Language Generation (nlg) sys- tems (Reiter and Dale, 2000). The gre task can informally be stated as follows. Given an intended referent (i.e., the object to be identi- fied) and a set of distractors (i.e., other objects that can be confused with the referent), find a description that allows a hearer to identify its referent uniquely (Dale, 1992). Such a descrip- tion is called a Distinguishing Description (dd). In practice, however, most gre algo- rithms build sets of semantic properties avail- able in a Knowledge Base (kb), rather than descriptions in natural language; surface issues are often ignored (exceptions are: (Stone and ∗ This work is supported by a University of Ab- erdeen Sixth Century Studentship, and EPSRC grant EP/E011764/1. ∗ c⃝ 2008. Licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported. Some rights reserved. Webber, 1998; Krahmer and Theune, 2002; Siddharthan and Copestake, 2004)). This is an important limitation, for example because ambiguities can be introduced in the step from properties to language descriptions. Such “sur- face ambiguities” take centerstage in this pa- per. More specifically, we shall be investigating situations where they lead to referential ambi- guity, that is, unclarity as to what the intended referent of a referring expression is. Example 1: Consider a scenario in which there are sheep and goats along with other an- imals, grazing in a meadow; some of the sheep and goats are black while others are either brown or yellow. Suppose our task is to single out the black sheep and black goats from the rest of the animals. Suppose an algorithm has generated the logical form1 (Black ∩ Sheep) ∪ (Black ∩ Goats), which could be realised as either the black sheep and the black goats or, more briefly, as the black sheep and goats. The latter np expresses two non-equivalent logical formulae: (i) (Black ∩ Sheep) ∪ Goats, and (ii) (Black ∩ Sheep) ∪ (Black ∩ Goats). Since both formulae correspond with a set of animals in the domain, referential ambiguity can result. On the other …

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