Natural Language Human-Computer Dialogue: Menu-Based Natural Language and Visual Performance

The present study was conducted to determine design principles for menu-based natural language (MBNL) interfaces and to provide evidence for the nature of visual search processes with menu-based systems. The effects of window size, window activity, and query length were investigated. Window size was manipulated as a between-subjects variable with three levels representing a sixteen-item window size, an eight-item window size, and a four-item window size. Window activity was manipulated as a within-subjects variable with two levels representing single active and multiple active windows. Query length was manipulated as a within-subjects variable with three levels representing one-, two-, and three-item query lengths. Thirty six subjects randomly assigned to three groups, based on the window size factor, performed queries with the three query lengths in both window activity conditions in counterbalanced order. It was found that two- and three-item queries were performed faster with single active windows. However, subjects rated multiple active windows as more \u27natural\u27. Query times also increased with query length and errors were most likely to occur on the longest query. Longer eye fixation durations were observed with the four-item window size. Fixation frequencies, fixation durations, dwell times, and relative dwell times all varied as a function of query length. Visual behavior also depended on which \u27area of interest\u27 subjects were viewing, and this effect interacted with window activity and query length. Finally, it was found that menus were not scanned randomly. However, scanpaths were less deterministic with multiple active windows and tended to become less constrained as query length increased. Based on the findings, human factors design principles were derived for application to MBNL interfaces

User-centred interface design for cross-language information retrieval

This paper reports on the user-centered design methodology and techniques used for the elicitation of user requirements and how these requirements informed the first phase of the user interface design for a Cross-Language Information Retrieval System. We describe a set of factors involved in analysis of the data collected and, finally discuss the implications for user interface design based on the findings

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Interaction Issues in Computer Aided Semantic\ud Annotation of Multimedia

The CASAM project aims to provide a tool for more efficient and effective annotation of multimedia documents through collaboration between a user and a system performing an automated analysis of the media content. A critical part of the project is to develop a user interface which best supports both the user and the system through optimal human-computer interaction. In this paper we discuss the work undertaken, the proposed user interface and underlying interaction issues which drove its development

Mobile Phone Text Processing and Question-Answering

Mobile phone text messaging between mobile users and information services is a growing area of Information Systems. Users may require the service to provide an answer to queries, or may, in wikistyle, want to contribute to the service by texting in some information within the service’s domain of discourse. Given the volume of such messaging it is essential to do the processing through an automated service. Further, in the case of repeated use of the service, the quality of such a response has the potential to benefit from a dynamic user profile that the service can build up from previous texts of the same user. This project will investigate the potential for creating such intelligent mobile phone services and aims to produce a computational model to enable their efficient implementation. To make the project feasible, the scope of the automated service is considered to lie within a limited domain of, for example, information about entertainment within a specific town centre. The project will assume the existence of a model of objects within the domain of discourse, hence allowing the analysis of texts within the context of a user model and a domain model. Hence, the project will involve the subject areas of natural language processing, language engineering, machine learning, knowledge extraction, and ontological engineering