Generating Referring Expressions in Open Domains

Generating Referring Expressions in Open Domains Advaith Siddharthan Ann Copestake Computer Science Department Computer Laboratory Columbia University University of Cambridge as372@cs.columbia.edu aac10@cl.cam.ac.uk Abstract We present an algorithm for generating referring expressions in open domains. Existing algorithms work at the semantic level and assume the avail- ability of a classification for attributes, which is only feasible for restricted domains. Our alterna- tive works at the realisation level, relies on Word- Net synonym and antonym sets, and gives equiva- lent results on the examples cited in the literature and improved results for examples that prior ap- proaches cannot handle. We believe that ours is also the first algorithm that allows for the incremen- tal incorporation of relations. We present a novel corpus-evaluation using referring expressions from the Penn Wall Street Journal Treebank. 1 Introduction Referring expression generation has historically been treated as a part of the wider issue of gener- ating text from an underlying semantic representa- tion. The task has therefore traditionally been ap- proached at the semantic level. Entities in the real world are logically represented; for example (ignor- ing quantifiers), a big brown dog might be repre- sented as big1(x) ∧ brown1(x) ∧ dog1(x), where the predicates big1, brown1 and dog1 represent dif- ferent attributes of the variable (entity) x. The task of referring expression generation has traditionally been framed as the identification of the shortest log- ical description for the referent entity that differen- tiates it from all other entities in the discourse do- main. For example, if there were a small brown dog (small1(x) ∧ brown1(x) ∧ dog1(x)) in context, the minimal description for the big brown dog would be big1(x) ∧ dog1(x)1. This semantic framework makes it difficult to ap- ply existing referring expression generation algo- rithms to the many regeneration tasks that are im- portant today; for example, summarisation, open- ended question answering and text simplification. Unlike in traditional generation, the starting point in 1The predicate dog1 is selected because it has a distin- guished status, referred to as type in Reiter and Dale (1992). One such predicate has to to be present in the description. these tasks is unrestricted text, rather than a seman- tic representation of a small domain. It is difficult to extract the required semantics from unrestricted text (this task would require sense disambiguation, among other issues) and even harder to construct a classification for the extracted predicates in the manner that existing approaches require (cf., §2). In this paper, we present an algorithm for generat- ing referring expressions in open domains. We dis- cuss the literature and detail the problems in apply- ing existing approaches to reference generation to open domains in §2. We then present our approach in §3, contrasting it with existing approaches. We extend our approach to handle relations in §3.3 and present a novel corpus-based evaluation on the Penn WSJ Treebank in §4. 2 Overview of Prior Approaches The incremental algorithm (Reiter and Dale, 1992) is the most widely discussed attribute selection algorithm. It takes as …

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