对话管理中基于槽特征有限状态自动机的方法研究

对话系统的研究已经成为人机交互技术发展的新热点。而对话管理则是其中最重要的组成部分．该文在当前对话管理的各种实现方法的基础上,提出了一种基于槽特征的自动机设计方法,其中应用了状态压缩和状态集、动作集的子空间划分。并着重以确认过程为例,阐述了确认策略控制函数及其对对话过程的影响．文中还提出了一种树形的意图分层结构,并将这种分层结构应用于主题检测与主题切换,成功解决了多主题对话系统的主题切换问题．最后,实验表明该文提出的设计方案在策略控制、主题检测与主题切换等方面具有较好性能,同时也具有一定扩展性．     

User-Tailored Planning of Mixed Initiative Information-Seeking Dialogues

Intelligent dialogue systems usually concentrate on user support at the level of the domain of discourse, following a plan-based approach. Whereas this is appropriate for collaborative planning tasks, the situation in interactive information retrieval systems is quite different: there is no inherent plan-goal hierarchy, and users are known to often opportunistically change their goals and strategies during and through interaction. We need to allow for mixed-initiative retrieval dialogues, where the system evaluates the user's individual dialogue behavior and performs situation-dependent interpretation of user goals, to determine when to take the initiative and to change the control of the dialogue, e.g., to propose (new) problem-solving strategies to the user. In this article, we present the dialogue planning component of a concept- oriented, logic-based retrieval system (MIRACLE). Users are guided through the global stages of the retrieval interaction but may depart, at any time, from this guidance and change the direction of the dialogue. When users submit ambiguous queries or enter unexpected dialogue control acts, abductive reasoning is used to generate interpretations of these user inputs in light of the dialogue history and other internal knowledge sources. Based on these interpretations, the system initiates a short dialogue offering the user suitable options and strategies for proceeding with the retrieval dialogue. Depending on the user's choice and constraints resulting from the history, the system adapts its strategy accordingly.     

陪护机器人口语对话系统设计与实现

针对陪护机器人口语对话系统的难点给出了相应的解决方案：以话题为单位的聊天机器人知识库构建规则、简单有效的句子相似度计算方法、准确高效的答案检索模块。通过对78个回合的实际聊天结果的统计分析,验证了解决方案的有效性;同时分析了方案的局限性。     

An Evidential Model for Tracking Initiative in Collaborative Dialogue Interactions

In this paper, we argue for the need to distinguish between task initiative and dialogue initiative, and present an evidential model for tracking shifts in both types of initiatives in collaborative dialogue interactions. Our model predicts the task and dialogue initiative holders for the next dialogue turn based on the current initiative holders and the effect that observed cues have on changing them. Our evaluation across various corpora shows that the use of cues consistently provides significant improvement in the system's prediction of task and dialogue initiative holders. Finally, we show how this initiative tracking model may be employed by a dialogue system to enable the system to tailor its responses to user utterances based on application domain, system's role in the domain, dialogue history, and user characteristics.     

User Modeling in Spoken Dialogue Systems to Generate Flexible Guidance

We address the issue of appropriate user modeling to generate cooperative responses to users in spoken dialogue systems. Unlike previous studies that have focused on a user’s knowledge, we propose more generalized modeling. We specifically set up three dimensions for user models: the skill level in use of the system, the knowledge level about the target domain, and the degree of urgency. Moreover, the models are automatically derived by decision tree learning using actual dialogue data collected by the system. We obtained reasonable accuracy in classification for all dimensions. Dialogue strategies based on user modeling were implemented on the Kyoto City Bus Information System that was developed at our laboratory. Experimental evaluations revealed that the cooperative responses adapted to each subject type served as good guides for novices without increasing the duration dialogue lasted for skilled users.     

汉语股票实时行情查询对话系统

介绍了一个用于股票实时行情查询的口语化的人机对话系统,该系统集成了语音识别、语言理解、对话控制等技术。文中定义了一个情景语义框架模型,较好地处理了口语理解系统的一些难点。     

Designing and Evaluating an Adaptive Spoken Dialogue System

Spoken dialogue system performance can vary widely for different users, as well for the same user during different dialogues. This paper presents the design and evaluation of an adaptive version of TOOT, a spoken dialogue system for retrieving online train schedules. Based on rules learned from a set of training dialogues, adaptive TOOT constructs a user model representing whether the user is having speech recognition problems as a particular dialogue progresses. Adaptive TOOT then automatically adapts its dialogue strategies based on this dynamically changing user model. An empirical evaluation of the system demonstrates the utility of the approach.     

基于两阶段分类的口语理解方法

口语理解是实现口语对话系统的关键技术之一.它主要面临两方面的挑战:1)稳健性,因为输入语句往往是病态的;2)可移植性,即口语理解单元应能够快速移植到新的领域和语言.提出了一种新的基于两阶段分类的口语理解方法:第1阶段为主题分类,用来识别用户输入语句的主题;第2阶段为主题相关的语义槽分类,根据识别的主题抽取相应的语义槽/值对.该方法能对用户输入语句进行深层理解,同时也能保持稳健性.它基本上是数据驱动的,而且训练数据的标记也比较容易,可方便地移植到新的领域和语言.实验分别在汉语交通查询领域和英语DARPA Communicator领域进行,结果表明了该方法的有效性.     

A Computational Mechanism for Initiative in Answer Generation

Initiative in dialogue can be regarded as the speaker taking the opportunity to contribute more information than was his obligation in a particular discourse turn. This paper describes the use of stimulus conditions as a computational mechanism for taking the initiative to provide unrequested information in responses to Yes–No questions, as part of a system for generating answers to Yes–No questions. Stimulus conditions represent types of discourse contexts in which a speaker is motivated to add unrequested information to his answer. Stimulus conditions may be triggered not only by the discourse context at the time when the question was asked, but also by the anticipated context resulting from providing part of the response. We define a set of stimulus conditions based upon previous linguistic studies and a corpus analysis, and describe how evaluation of these stimulus conditions makes use of information from a User Model. Also, we show how the stimulus conditions are used by the generation component of the system. An evaluation was conducted of the implemented system. The results indicate that the responses generated by our system containing extra information provided on the basis of this initiative mechanism are viewed more favorably by users than responses without the extra information.     

Effects of Speech Recognition Accuracy on the Performance of DARPA Communicator Spoken Dialogue Systems

The DARPA Communicator program explored ways to construct better spoken-dialogue systems, with which users interact via speech alone to perform relatively complex tasks such as travel planning. During 2000 and 2001 two large data sets were collected from sessions in which paid users did travel planning using the Communicator systems that had been built by eight research groups. The research groups improved their systems intensively during the ten months between the two data collections. In this paper, we analyze these data sets to estimate the effects of speech recognition accuracy, as measured by Word Error Rate (WER), on other metrics. The effects that we found were linear. We found correlation between WER and Task Completion, and that correlation, unexpectedly, remained more or less linear even for high values of WER. The picture for User Satisfaction metrics is more complex: we found little effect of WER on User Satisfaction for WER less than about 35 to 40% in the 2001 data. The size of the effect of WER on Task Completion was less in 2001 than in 2000, and we believe this difference is due to improved strategies for accomplishing tasks despite speech recognition errors, which is an important accomplishment of the research groups who built the Communicator implementations. We show that additional factors must account for much of the variability in task success, and we present multivariate linear regression models for task success on the 2001 data. We also discuss the apparent gaps in the coverage of our metrics for spoken dialogue systems.     

An Intelligent Man-Machine Dialogue System Based on AI Planning

We describe the modular architecture of a generic dialogue system that assists a user/operator in performing a task with a tool. This coaching system is named CALLIOPE after the Greek goddess of eloquence. It aims at being an active partner in an intelligent man-machine dialogue. The intelligent dimension of the coaching system is reflected by its ability to adapt to the user and the situation at hand. The CALLIOPE system contains an explicit user model and world model to situate its dialogue actions. A plan library allows it to follow loosely predetermined dialogue scenarios.The heart of the coaching system is an AI planning module, which plans a series of dialogue actions. We present a coherent set of three dialogue or speech actions that will make up the physical form of the man-machine communication.The use of the AI planning paradigm as a basis for man-machine interaction is motivated by research in various disciplines, as e.g., AI, Cognitive Science and Social Sciences. Starting from the man-man communication metaphor, we can view the thinking before speaking of a human communication partner as constructing an underlying plan which is responsible for the purposiveness, the organisation and the relevance of the communication.CALLIOPE has been fully implemented and tested on theoretical examples. At present, also three tailored versions of CALLIOPE are in operational use in different industrial application domains: operator support for remedying tasks in chemical process industry, operator support for a combined task of planning, plan execution and process control in the area of chemical process development, and thirdly decision support in production scheduling.     

Users' Conceptions of Voice-Operated Information Services

When users interact with a voice-operated service, they bring along their habits as well as their expectations from experience with human-human dialogues, with the domain, and with other systems and services. In addition, users' expectations are further shaped while using a system. The present study explores the extent to which user-system interaction, and in particular difficulties in the interaction, are affected by users' expectations and (mis)conceptions of the service, and how these expectations evolve during use. In an exploratory study, twenty subjects queried two different train travel information services. A semi-structured interview was held on subjects' dialogues with the systems, by replaying the recordings together with the subjects. In interacting with voice-operated services, users appear to draw from various sources of experience. Users' misconceptions and misunderstandings of the system lead to various problems in interaction, such as undesired travel suggestions and irritation. The implications for the design of voice-operated services are discussed.     

Shifting the Design Philosophy of Spoken Natural Language Dialogue: From Invisible to Transparent Systems

Spoken natural language interfaces are characterized by a high degree of opacity. This characteristic leads many new users to converse with the machine on the basis of an inaccurate image of the system, which results in numerous dysfunctions and failures in the human-machine spoken dialogue. The application of the principle of transparency aims to correct this problem by enabling the system to make it “visible” (it can, so to speak, be seen “from the inside”). This report presents a study that compares two versions of the same spoken dialogue system that differ only by some of their dialogue strategies. In particular, one version of this system starts the dialogue with a recall of the main system's function followed by an open-ended prompt, while the other version only produces an open-ended prompt (What can I do for you?). Moreover, one version of this system makes explicit some possible actions in specific dialogue contexts, while the other version leaves these possible actions implicit. These strategies were compared by having a group of 28 users test them in real conditions of use. The results highlight that transparency strategies can help users in formulating simple and acceptable requests, satisfying their goals more easily while provoking less recognition and comprehension errors.     

一种有效的大型资料全文检索技术

针对大型资料全文检索的速度要求，在分析现有曲型算法的基础上，提出了文本一模式匹配向量的概念。     

An Analysis of Initiative Selection in Collaborative Task-Oriented Discourse

In this paper we propose a number of principles and conjectures for mixed-initiative collaborative dialogs. We explore some methodologies for managing initiative between conversational participants. We mathematically analyze specific initiative-changing mechanisms based on a probabilistic knowledge base and user model. We look at the role of negotiation in managing initiative and quantify how the negotiation process is useful toward modifying user models. Some experimental results using computer–computer simulations are presented along with some discussion of how such studies are useful toward building human–computer systems.     

An Engineering Approach to Cooperating Agents for Distributed Information Systems

This paper presents a multi-agent model of a distributed information system, using what is described as an engineering approach to real world application environment. The objective is to define, using proven ideas in the industrial context, the agent-based behaviour of the distributed system, which must operate correctly and effectively in an error-prone environment. Issues such as stability, robustness and scalability have also been addressed, along with some new ideas on a high-level communication strategies, as distinct from protocol-based communications. The work is being carried out under the DREAM theme at Keele, an earlier version of the approach having been successfully applied to agent-based manufacturing in an international project called HMS, in which some of the world’s major manufacturing industries participated.     

基于模糊语言方法的信息检索系统的研究

该文提出了一个基于模糊语言方法的信息检索系统模型。该系统分为查询界面子系统、数据库子系统和检索子系统三大部分。在查询界面子系统,用布尔表达式表示用户的查询请求,并对每个查询关键词赋予了两种不同语义的语言值权重,该权重表达了用户的模糊检索要求;在数据库子系统,用索引词一文档模糊矩阵表示待检索的文档,对每个索引词。根据其在文档中的出现频率大小。引入了数值权重;在检索子系统,运用模糊语言方法,对用户输入的布尔查询表达式与索引词一文档模糊矩阵进行自底向上的模糊匹配,最后返回满足用户要求的检索结果。相对于传统的基于查询关键词精确匹配的检索系统而言,该系统能较好地满足用户查询要求中的灵活性。     

Enhancing Workflow Management in the Manufacturing Information System of a Small-Medium Enterprise: An Agent-Based Approach

One shortcoming of manufacturing information systems is their inability to integrate and to enhance different information and related functionalities, such as those found in workflow management. Software agents, once added as a new software layer to an existing system, can overcome this problem. Of the fields applying software agent technology, manufacturing, and especially workflow management, is perhaps the most promising for the development of distributed systems. This potential is reflected in the several ongoing research projects that focus on workflow management and share the final aim of integrating and coordinating plant and business activities.The approach presented in this paper can be regarded as an agent-based architecture applied to an existing workflow management system in order to manage new functionalities, e.g., customer relationship management in electronic commerce. This approach is particularly suitable for small medium enterprises with simple manufacturing information requirements and simple or nonexistent computer based workflow management.The model and some important features of system implementation, such as the knowledge modeling graphical user interface, the communication model, and a simple practical example of the architecture's use, are described.