Cost-based attribute selection for GRE (GRAPH-SC/GRAPH-FP)

In this paper we discuss several approaches to the problem of content determination for the generation of referring expressions (GRE) using the Graphbased framework of Krahmer et al. (2003). This work was carried out in the context of the First NLG Shared Task and Evaluation Challenge on Attribute Selection for Referring Expression Generation

Controlling redundancy in referring expressions

Krahmer et al.’s (2003) graph-based framework provides an elegant and flexible approach to the generation of referring expressions. In this paper, we present the first reported study that systematically investigates how to tune the parameters of the graph-based framework on the basis of a corpus of human-generated descriptions. We focus in particular on replicating the redundant nature of human referring expressions, whereby properties not strictly necessary for identifying a referent are nonetheless included in descriptions. We show how statistics derived from the corpus data can be integrated to boost the framework’s performance over a non-stochastic baseline

Realizing the Costs: Template-Based Surface Realisation in the GRAPH Approach to Referring Expression Generation

We describe a new realiser developed for the TUNA 2009 Challenge, and present its evaluation scores on the development set, showing a clear increase in performance compared to last year’s simple realiser