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

A New Model for Generating Multimodal Referring Expressions

We present a new algorithm for the generation of multimodal referring expressions (combining language and deictic gestures).1 The approach differs from earlier work in that we allow for various gradations of preciseness in pointing, ranging from unambiguous to vague pointing gestures. The model predicts that linguistic properties realized in the generated expression are co-dependent on the kind of pointing gesture included. The decision to point is based on a tradeoff between the costs of pointing and the costs of linguistic properties, where both kinds of costs are computed in empirically motivated ways. The model has been implemented using a graph-based generation algorithm

Does Size Matter – How Much Data is Required to Train a REG Algorithm?

In this paper we investigate how much data is required to train an algorithm for attribute selection, a subtask of Referring Expressions Generation (REG). To enable comparison between different-sized training sets, a systematic training method was developed. The results show that depending on the complexity of the domain, training on 10 to 20 items may already lead to a good performance

The automatic generation of narratives

We present the Narrator, a Natural Language Generation component used in a digital storytelling system. The system takes as input a formal representation of a story plot, in the form of a causal network relating the actions of the characters to their motives and their consequences. Based on this input, the Narrator generates a narrative in Dutch, by carrying out tasks such as constructing a Document Plan, performing aggregation and ellipsis and the generation of appropriate referring expressions. We describe how these tasks are performed and illustrate the process with examples, showing how this results in the generation of coherent and well-formed narrative texts

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

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