Desiderata for an Every Citizen Interface to the National Information Infrastructure: Challenges for NLP

In this paper, I provide desiderata for an interface that would enable ordinary people to properly access the capabilities of the NII. I identify some of the technologies that will be needed to achieve these desiderata, and discuss current and future research directions that could lead to the development of such technologies. In particular, I focus on the ways in which theory and techniques from natural language processing could contribute to future interfaces to the NII. Introduction The evolving national information infrastructure (NII) has made available a vast array of on-line services and networked information resources in a variety of forms (text, speech, graphics, images, video). At the same time, advances in computing and telecommunications technology have made it possible for an increasing number of households to own (or lease or use) powerful personal computers that are connected to this resource. Accompanying this progress is the expectation that people will be able to more..

Information Presentation in Spoken Dialogue Systems

To tackle the problem of presenting a large number of options in spoken dialogue systems, we identify compelling options based on a model of user preferences, and present tradeoffs between alternative options explicitly. Multiple attractive options are structured such that the user can gradually refine her request to find the optimal tradeoff. We show that our approach presents complex tradeoffs understandably, increases overall user satisfaction, and significantly improves the user's overview of the available options. Moreover, our results suggest that presenting users with a brief summary of the irrelevant options increases users' confidence in having heard about all relevant options

An Empirical Study of the Influence of User Tailoring on Evaluative Argument Effectiveness

The ability to generate effective evaluative arguments is critical for systems intended to advise and persuade their users. We have developed a system that generates evaluative arguments that are tailored to the user, properly arranged and concise. We have also devised an evaluation framework in which the effectiveness of evaluative arguments can be measured with real users. This paper presents the results of a formal experiment we performed in our framework to verify the influence of user tailoring on argument effectiveness.

Learning Features that Predict Cue Usage

Our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in order to aid in the automatic generation of explanations. Previous attempts to devise rules for text generation were based on intuition or small numbers of constructed examples. We apply a machine learning program, C4.5, to induce decision trees for cue occurrence and placement from a corpus of data coded for a variety of features previously thought to affect cue usage. Our experiments enable us to identify the features with most predictive power, and show that machine learning can be used to induce decision trees useful for text generation.Comment: 10 pages, 2 Postscript figures, uses aclap.sty, psfig.te

Towards a Principled Representation of Discourse Plans

We argue that discourse plans must capture the intended causal and decompositional relations between communicative actions. We present a planning algorithm, DPOCL, that builds plan structures that properly capture these relations, and show how these structures are used to solve the problems that plagued previous discourse planners, and allow a system to participate effectively and flexibly in an ongoing dialogue.Comment: requires cogsci94.sty, psfig.st

Exploring User Satisfaction in a Tutorial Dialogue System

Abstract User satisfaction is a common evaluation metric in task-oriented dialogue systems, whereas tutorial dialogue systems are often evaluated in terms of student learning gain. However, user satisfaction is also important for such systems, since it may predict technology acceptance. We present a detailed satisfaction questionnaire used in evaluating the BEETLE II system (REVU-NL), and explore the underlying components of user satisfaction using factor analysis. We demonstrate interesting patterns of interaction between interpretation quality, satisfaction and the dialogue policy, highlighting the importance of more finegrained evaluation of user satisfaction

The Impact of Interpretation Problems on Tutorial Dialogue

Supporting natural language input may improve learning in intelligent tutoring systems. However, interpretation errors are unavoidable and require an effective recovery policy. We describe an evaluation of an error recovery policy in the BEE-TLE II tutorial dialogue system and discuss how different types of interpretation problems affect learning gain and user satisfaction. In particular, the problems arising from student use of non-standard terminology appear to have negative consequences. We argue that existing strategies for dealing with terminology problems are insufficient and that improving such strategies is important in future ITS research.

Toward a Synthesis of Two Accounts of Discourse Structure

... In this paper, we argue that the main theories representing these two approaches, RST (Mann and Thompson 1988) and G&S (Grosz and Sidner 1986), make similar claims about how speakers' intentions determine a structure of their discourse. The similarity occurs because the nucleus-satellite relation among text spans in RST corresponds to the dominance relation among intentions in G&S. Building on this similarity, we sketch a partial mapping between the two theories to show that the main points of the two theories are equivalent. Furthermore, the additional claims found in only RST or only G&S are largely consistent. The issue of what structure is determined by semantic (domain) relations in the discourse and how this structure might be related to the intentional structure is discussed. We suggest the synthesis of the two theories would be useful to researchers in both natural language interpretation and generation