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

Fish or Fowl: A Wizard of Oz Evaluation of Dialogue Strategies in the Restaurant Domain

Recent work on evaluation of spoken dialogue systems suggests that the information presentation phase of complex dialogues is often the primary contributor to dialogue duration. This indicates that better algorithms are needed for the presentation of complex information in speech. Currently however we lack data about the tasks and dialogue strategies on which to base such algorithms. In this paper, we describe a Wizard of Oz tool and a study which applies user models based on multi-attribute decision theory to the problem of generating tailored and concise system responses for a spoken dialogue system. The resulting Wizard corpus will be distributed by the LDC as part of our work on the ISLE project

Speech-plans: Generating evaluative responses in spoken dialogue

Recent work on evaluation of spoken dialogue systems indicates that better algorithms are needed for the presentation of complex information in speech. Current dialogue systems often rely on presenting sets of options and their attributes sequentially. This places a large memory burden on users, who have to remember complex trade-offs between multiple options and their attributes. To address these problems we build on previous work using multiattribute decision theory to devise speech-planning algorithms that present usertailored summaries, comparisons and recommendations that allow users to focus on critical differences between options and their attributes. We discuss the differences between speech and text planning that result from the particular demands of the speech situation.

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

Individual and Domain Adaptation in Sentence Planning for Dialogue

One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations

From Monologue to Dialogue: Natural Language Generation in OVIS

This paper describes how a language generation system that was originally designed for monologue generation, has been adapted for use in the OVIS spoken dialogue system. To meet the requirement that in a dialogue, the system's utterances should make up a single, coherent dialogue turn, several modifications had to be made to the system. The paper also discusses the influence of dialogue context on information status, and its consequences for the generation of referring expressions and accentuation

Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems

This paper presents the Frames dataset (Frames is available at http://datasets.maluuba.com/Frames), a corpus of 1369 human-human dialogues with an average of 15 turns per dialogue. We developed this dataset to study the role of memory in goal-oriented dialogue systems. Based on Frames, we introduce a task called frame tracking, which extends state tracking to a setting where several states are tracked simultaneously. We propose a baseline model for this task. We show that Frames can also be used to study memory in dialogue management and information presentation through natural language generation