10 research outputs found

    A computational framework for mixed-initiative dialog modeling.

    Get PDF
    Chan, Shuk Fong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 114-122).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Thesis Contributions --- p.5Chapter 1.3 --- Thesis Outline --- p.9Chapter 2 --- Background --- p.10Chapter 2.1 --- Mixed-Initiative Interactions --- p.11Chapter 2.2 --- Mixed-Initiative Spoken Dialog Systems --- p.14Chapter 2.2.1 --- Finite-state Networks --- p.16Chapter 2.2.2 --- Form-based Approaches --- p.17Chapter 2.2.3 --- Sequential Decision Approaches --- p.18Chapter 2.2.4 --- Machine Learning Approaches --- p.20Chapter 2.3 --- Understanding Mixed-Initiative Dialogs --- p.24Chapter 2.4 --- Cooperative Response Generation --- p.26Chapter 2.4.1 --- Plan-based Approach --- p.27Chapter 2.4.2 --- Constraint-based Approach --- p.28Chapter 2.5 --- Chapter Summary --- p.29Chapter 3 --- Mixed-Initiative Dialog Management in the ISIS system --- p.30Chapter 3.1 --- The ISIS Domain --- p.31Chapter 3.1.1 --- System Overview --- p.31Chapter 3.1.2 --- Domain-Specific Constraints --- p.33Chapter 3.2 --- Discourse and Dialog --- p.34Chapter 3.2.1 --- Discourse Inheritance --- p.37Chapter 3.2.2 --- Mixed-Initiative Dialogs --- p.41Chapter 3.3 --- Challenges and New Directions --- p.45Chapter 3.3.1 --- A Learning System --- p.46Chapter 3.3.2 --- Combining Interaction and Delegation Subdialogs --- p.49Chapter 3.4 --- Chapter Summary --- p.57Chapter 4 --- Understanding Mixed-Initiative Human-Human Dialogs --- p.59Chapter 4.1 --- The CU Restaurants Domain --- p.60Chapter 4.2 --- "Task Goals, Dialog Acts, Categories and Annotation" --- p.61Chapter 4.2.1 --- Task Goals and Dialog Acts --- p.61Chapter 4.2.2 --- Semantic and Syntactic Categories --- p.64Chapter 4.2.3 --- Annotating the Training Sentences --- p.65Chapter 4.3 --- Selective Inheritance Strategy --- p.67Chapter 4.3.1 --- Category Inheritance Rules --- p.67Chapter 4.3.2 --- Category Refresh Rules --- p.73Chapter 4.4 --- Task Goal and Dialog Act Identification --- p.78Chapter 4.4.1 --- Belief Networks Development --- p.78Chapter 4.4.2 --- Varying the Input Dimensionality --- p.80Chapter 4.4.3 --- Evaluation --- p.80Chapter 4.5 --- Procedure for Discourse Inheritance --- p.83Chapter 4.6 --- Chapter Summary --- p.86Chapter 5 --- Cooperative Response Generation in Mixed-Initiative Dialog Modeling --- p.88Chapter 5.1 --- System Overview --- p.89Chapter 5.1.1 --- State Space Generation --- p.89Chapter 5.1.2 --- Task Goal and Dialog Act Generation for System Response --- p.92Chapter 5.1.3 --- Response Frame Generation --- p.93Chapter 5.1.4 --- Text Generation --- p.100Chapter 5.2 --- Experiments and Results --- p.100Chapter 5.2.1 --- Subjective Results --- p.103Chapter 5.2.2 --- Objective Results --- p.105Chapter 5.3 --- Chapter Summary --- p.105Chapter 6 --- Conclusions --- p.108Chapter 6.1 --- Summary --- p.108Chapter 6.2 --- Contributions --- p.110Chapter 6.3 --- Future Work --- p.111Bibliography --- p.113Chapter A --- Domain-Specific Task Goals in CU Restaurants Domain --- p.123Chapter B --- Full list of VERBMOBIL-2 Dialog Acts --- p.124Chapter C --- Dialog Acts for Customer Requests and Waiter Responses in CU Restaurants Domain --- p.125Chapter D --- The Two Grammers for Task Goal and Dialog Act Identifi- cation --- p.130Chapter E --- Category Inheritance Rules --- p.143Chapter F --- Category Refresh Rules --- p.149Chapter G --- Full list of Response Trigger Words --- p.154Chapter H --- Evaluation Test Questionnaire for Dialog System in CU Restaurants Domain --- p.159Chapter I --- Details of the statistical testing Regarding Grice's Maxims and User Satisfaction --- p.16

    Interactions in Virtual Worlds:Proceedings Twente Workshop on Language Technology 15

    Get PDF

    The significance of silence. Long gaps attenuate the preference for ‘yes’ responses in conversation.

    Get PDF
    In conversation, negative responses to invitations, requests, offers and the like more often occur with a delay – conversation analysts talk of them as dispreferred. Here we examine the contrastive cognitive load ‘yes’ and ‘no’ responses make, either when given relatively fast (300 ms) or delayed (1000 ms). Participants heard minidialogues, with turns extracted from a spoken corpus, while having their EEG recorded. We find that a fast ‘no’ evokes an N400-effect relative to a fast ‘yes’, however this contrast is not present for delayed responses. This shows that an immediate response is expected to be positive – but this expectation disappears as the response time lengthens because now in ordinary conversation the probability of a ‘no’ has increased. Additionally, however, 'No' responses elicit a late frontal positivity both when they are fast and when they are delayed. Thus, regardless of the latency of response, a ‘no’ response is associated with a late positivity, since a negative response is always dispreferred and may require an account. Together these results show that negative responses to social actions exact a higher cognitive load, but especially when least expected, as an immediate response

    Advanced techniques for personalized, interactive question answering

    Get PDF
    Using a computer to answer questions has been a human dream since the beginning of the digital era. A first step towards the achievement of such an ambitious goal is to deal with naturallangilage to enable the computer to understand what its user asks. The discipline that studies the conD:ection between natural language and the represen~ tation of its meaning via computational models is computational linguistics. According to such discipline, Question Answering can be defined as the task that, given a question formulated in natural language, aims at finding one or more concise answers in the form of sentences or phrases. Question Answering can be interpreted as a sub-discipline of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text. Although it is widely accepted that Question Answering represents a step beyond standard infomiation retrieval, allowing a more sophisticated and satisfactory response to the user's information needs, it still shares a series of unsolved issues with the latter. First, in most state-of-the-art Question Answering systems, the results are created independently of the questioner's characteristics, goals and needs. This is a serious limitation in several cases: for instance, a primary school child and a History student may need different answers to the questlon: When did, the Middle Ages begin? Moreover, users often issue queries not as standalone but in the context of a wider information need, for instance when researching a specific topic. Although it has recently been proposed that providing Question Answering systems with dialogue interfaces would encourage and accommodate the submission of multiple related questions and handle the user's requests for clarification, interactive Question Answering is still at its early stages: Furthermore, an i~sue which still remains open in current Question Answering is that of efficiently answering complex questions, such as those invoking definitions and descriptions (e.g. What is a metaphor?). Indeed, it is difficult to design criteria to assess the correctness of answers to such complex questions. .. These are the central research problems addressed by this thesis, and are solved as follows. An in-depth study on complex Question Answering led to the development of classifiers for complex answers. These exploit a variety of lexical, syntactic and shallow semantic features to perform textual classification using tree-~ernel functions for Support Vector Machines. The issue of personalization is solved by the integration of a User Modelling corn': ponent within the the Question Answering model. The User Model is able to filter and fe-rank results based on the user's reading level and interests. The issue ofinteractivity is approached by the development of a dialogue model and a dialogue manager suitable for open-domain interactive Question Answering. The utility of such model is corroborated by the integration of an interactive interface to allow reference resolution and follow-up conversation into the core Question Answerin,g system and by its evaluation. Finally, the models of personalized and interactive Question Answering are integrated in a comprehensive framework forming a unified model for future Question Answering research

    Learning to Behave: Internalising Knowledge

    Get PDF

    Annual Report

    Get PDF

    Recent developments in the experimental "Waxholm" dialog system

    No full text

    Recent developments in the experimental "Waxholm" dialog system

    No full text
    Recently we have begun to build the basic tools for a genetic speech-dialog system. The main modules, their function and internal ccommunication have been specified. The different components ~e connected through a computer network. A preliminary version of the system has been tested, using simplified versions of the modules. The dialog component of the system is described by a dialog grammar with the help of semantic features. Probabilities are also used in this process. We will give a general overview of the system and describe some of the components in more detail. Application-specific data are collected with the help of Wizard-of-Oz techniques. Currently the system is used during the data collection and the bionic wizard replaces only the speech-recognition module. 1
    corecore