Towards automated generation of scripted dialogue: some time-honoured strategies

The main aim of this paper is to introduce automated generation of scripted dialogue as a worthwhile topic of investigation. In particular the fact that scripted dialogue involves two layers of communication, i.e., uni-directional communication between the author and the audience of a scripted dialogue and bi-directional pretended communication between the characters featuring in the dialogue, is argued to raise some interesting issues. Our hope is that the combined study of the two layers will forge links between research in text generation and dialogue processing. The paper presents a first attempt at creating such links by studying three types of strategies for the automated generation of scripted dialogue. The strategies are derived from examples of human-authored and naturally occurring dialogue

Reference and the facilitation of search in spatial domains

This is a pre-final version of the article, whose official publication is expected in the winter of 2013-14.Peer reviewedPreprin

Vagueness as Cost Reduction : An Empirical Test

This work was funded in part by an EPSRC Platform Grant awarded to the NLG group at Aberdeen.Publisher PD

Production of Referring Expressions for an Unknown Audience : a Computational Model of Communal Common Ground

The research reported in this article is based on the Ph.D. project of Dr. RK, which was funded by the Scottish Informatics and Computer Science Alliance (SICSA). KvD acknowledges support from the EPSRC under the RefNet grant (EP/J019615/1).Peer reviewedPublisher PD

Generating under global constraints: the case of scripted dialogue

Recently, the view of Natural Language Generation (NLG) as a Constraint Satisfaction Problem (CSP) has seen something of a revival. The aim of this paper is to examine the issues that arise when nlg is viewed as a CSP, and to introduce a novel application of constraint-based NLG, namely the ScriptedDialogue. ScriptedDialogue shares a number of crucial features with discourse, which make it possible to control the global properties of a computer-generated dialogue in the same way as those of a generated discourse. We pay particular attention to the use of soft constraints for enforcing global properties of text and dialogue. Because there has been little research into the formal properties of soft constraints in relation to generation, we start out with a theoretical exploration. We argue that, when multiple constraints are involved, it is important to define properly what is being optimised before proposing specific algorithms, and we argue that such definitions are often lacking in csp-based nlg. We show that it can be difficult (and sometimes even impossible) to guarantee satisfaction of global constraints by following local strategies. Based on these difficulties, we propose a novel approach to the generation of discourse and dialogue which combines csp solving with revision. Scripted Dialogue is used to illustrate this approach, which is compared with alternatives such as monitoring and estimation

Lexical choice and conceptual perspective in the generation of plural referring expressions

A fundamental part of the process of referring to an entity is to categorise it (for instance, as the woman). Where multiple categorisations exist, this implicitly involves the adoption of a conceptual perspective. A challenge for the automatic Generation of Referring Expressions is to identify a set of referents coherently, adopting the same conceptual perspective. We describe and evaluate an algorithm to achieve this. The design of the algorithm is motivated by the results of psycholinguistic experiments.peer-reviewe

Generating Easy References : the case of document deixis.

This work is part of an ongoing PhD project of the first author and it has been supported by the CNPq, the Brazilian Research Council.Publisher PD

Does ChatGPT have Theory of Mind?

Theory of Mind (ToM) is the ability to understand human thinking and decision-making, an ability that plays a crucial role in social interaction between people, including linguistic communication. This paper investigates to what extent recent Large Language Models in the ChatGPT tradition possess ToM. We posed six well-known problems that address biases in human reasoning and decision making to two versions of ChatGPT and we compared the results under a range of prompting strategies. While the results concerning ChatGPT-3 were somewhat inconclusive, ChatGPT-4 was shown to arrive at the correct answers more often than would be expected based on chance, although correct answers were often arrived at on the basis of false assumptions or invalid reasoning