Information Seeking Spoken Dialogue Systems— Part II: Multimodal Dialogue

For pt.1see ibid., vol. 9, p. 3 (2007). In this paper, the task and user interface modules of a multimodal dialogue system development platform are presented. The main goal of this work is to provide a simple, application-independent solution to the problem of multimodal dialogue design for information seeking applications. The proposed system architecture clearly separates the task and interface components of the system. A task manager is designed and implemented that consists of two main submodules: the electronic form module that handles the list of attributes that have to be instantiated by the user, and the agenda module that contains the sequence of user and system tasks. Both the electronic forms and the agenda can be dynamically updated by the user. Next a spoken dialogue module is designed that implements the speech interface for the task manager. The dialogue manager can handle complex error correction and clarification user input, building on the semantics and pragmatic modules presented in Part I of this paper. The spoken dialogue system is evaluated for a travel reservation task of the DARPA Communicator research program and shown to yield over 90% task completion and good performance for both objective and subjective evaluation metrics. Finally, a multimodal dialogue system which combines graphical and speech interfaces, is designed, implemented and evaluated. Minor modifications to the unimodal semantic and pragmatic modules were required to build the multimodal system. It is shown that the multimodal system significantly outperforms the unimodal speech-only system both in terms of efficiency (task success and time to completion) and user satisfaction for a travel reservation task     

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.     

Reasoning with Intensional Negative Adjectivals: Semantics, Pragmatics, and Context

Intensional negative adjectives alleged , artificial , fake , false , former , and toy are unusual adjectives that depending on context may or may not be restricting functions. A formal theory of their semantics, pragmatics, and context that uniformly accounts for their complex mathematical and computational characteristics and captures some peculiarities of individual adjectives is presented.
Such adjectives are formalized as new concept builders, negation‐like functions that operate on the values of intensional properties of the concepts denoted by their arguments and yield new concepts whose intensional properties have values consistent with the negation of the old values. Understanding these new concepts involves semantics, pragmatics and context‐dependency of natural language. It is argued that intensional negative adjectives can be viewed as a special‐purpose, weaker, conntext‐dependent negationin natural language. The theory explains and predicts many inferences licensed by expressions involving such adjectives. Implementation of sample examples demonstrates its computational feasibility. Computation of context‐dependent interpretation is discussed.
The theory allows one to enhance a knowledge representation system with similar concept building, negation‐like, context‐dependent functions, the availability of which appears to be a distinct characteristic of natural languages.     

Focus interpretation in Thetic statements: Alternative Semantics and Optimality Theory Pragmatics

Broad focus (or informational integration or nonautonomy) is lexically and contextually constrained, but these constraints are not well understood. On a standard theory of focus interpretation, the presupposition of a broad focus is verified whenever those of two narrow foci are. I argue that to account for cases where two narrow foci are preferred, it is necessary to assume that broad focus competes with two narrow foci and implicates the opposite of what they presuppose. Central constraints on thetic statements are thus accounted for in an Optimality Theory (OT) enriched Alternative Semantics.     

Addressed Term Rewriting Systems: Syntax, Semantics, and Pragmatics: Extended Abstract

We present a formalism called Addressed Term Rewriting Systems, which can be used to define the operational semantics of programming languages, especially those involving sharing, recursive computations and cyclic data structures. Addressed Term Rewriting Systems are therefore well suited for describing object-based languages, as for instance the family of languages called , involving both functional and object-based features.     

三种不同类型语音对话系统的分析与研究

口语对话系统一直是计算机科学领域人类语言技术的热点,能够应用于不同的领域并且具备广阔的前景。将分析国外不同领域的三种典型会话系统：CommandTalk、ITSPOKE 和NICE。将从使用范围与交互方式、语音识别、对话管理、语音合成等几方面分析和研究这三种来自不同领域的对话系统,并提出观点和见解,为国内的口语对话系统研究和开发提供一定的参考和建议。     

An Infornation-Theoretic Analysis of Relational Databases—Part I: Data Dependencies and Information Metric

Database design is based on the concept of data dependency, which is the interrelationship between data contained in various sets of attributes. In particular, functional, multivalued and acyclic join, dependencies play an essential role in the design of database schemas. The basic definition of an information metric and how this notion can be used in relational database are discussed in this paper. We use Shannon entropy as an information metric to quantify the information associated with a set of attributes. Thus, we prove that data dependencies can be formulated in terms of entropies. These formulas make the numerical computation and testing of data dependencies feasible. Among the different types of data dependencies, the acyclic join dependency is most important to the design of a relational database schema. The acyclic join dependency, with multivalued dependency as a special case, impose a constraint on the information-preserving decomposition of a relation. It is interesting that this constraint on a relation is similar to Gibbs' condition for separating physical systems in statistical mechanics. They both assert that entropy is preserved during the decomposition process. That is, the entropies of the corresponding set of attributes must satisfy the inclusion–exclusion identity.     

Box-Jenkins identification revisited—Part I: Theory

In classical time domain Box-Jenkins identification discrete-time plant and noise models are estimated using sampled input/output signals. The frequency content of the input/output samples covers uniformly the whole unit circle in a natural way, even in case of prefiltering. Recently, the classical time domain Box-Jenkins framework has been extended to frequency domain data captured in open loop. The proposed frequency domain maximum likelihood (ML) solution can handle (i) discrete-time models using data that only covers a part of the unit circle, and (ii) continuous-time models. Part I of this series of two papers (i) generalizes the frequency domain ML solution to the closed loop case, and (ii) proves the properties of the ML estimator under non-standard conditions. Contrary to the classical time domain case it is shown that the controller should be either known or estimated. The proposed ML estimators are applicable to frequency domain data as well as time domain data.     

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.     

Dialogue Games for Inconsistent and Biased Information

In this article, a dialogue game is presented in which coherent conversational sequences with inconsistent and biased information are described at the speech act level. Inconsistent and biased information is represented with bilattice structures, and based on these bilattice structures, a multi-valued logic is defined that makes it possible to describe a dialogue game in which agents can communicate about their cognitive states with inconsistent and biased information. A dialogue game is formalized by, first, defining the agent's cognitive state as a set of multi-valued theories, second, by defining the dialogue rules that prescribe permissible communicative acts based on the agent's cognitive state, and last, by defining update rules that change the agent's cognitive state as a result of communicative acts. We show that an example dialogue with inconsistent and biased information can be derived from our dialogue game.     

Piezoresistive Cantilever Performance—Part I: Analytical Model for Sensitivity

An accurate analytical model for the change in resistance of a piezoresistor is necessary for the design of silicon piezoresistive transducers. Ion implantation requires a high-temperature oxidation or annealing process to activate the dopant atoms, and this treatment results in a distorted dopant profile due to diffusion. Existing analytical models do not account for the concentration dependence of piezoresistance and are not accurate for nonuniform dopant profiles. We extend previous analytical work by introducing two nondimensional factors, namely, the efficiency and geometry factors. A practical benefit of this efficiency factor is that it separates the process parameters from the design parameters; thus, designers may address requirements for cantilever geometry and fabrication process independently. To facilitate the design process, we provide a lookup table for the efficiency factor over an extensive range of process conditions. The model was validated by comparing simulation results with the experimentally determined sensitivities of piezoresistive cantilevers. We performed 9200 TSUPREM4 simulations and fabricated 50 devices from six unique process flows; we systematically explored the design space relating process parameters and cantilever sensitivity. Our treatment focuses on piezoresistive cantilevers, but the analytical sensitivity model is extensible to other piezoresistive transducers such as membrane pressure sensors. $hfill$[2009-0104]      

融合马尔科夫决策过程与信息熵的对话策略

对话策略是人机对话系统中的重要组成成分,其性能的优劣直接影响对话系统的性能.在面对完全没有数据的冷启动场景时,收集对话数据进行对话策略学习的过程非常复杂和耗时.为在冷启动场景下能够保持良好性能,提出一种融合马尔科夫决策过程与信息熵的对话算法.利用马尔科夫决策过程快速获得下一步最优对话状态,并结合知识库通过引入属性信息熵方法排除多个状态值函数相同的最优状态,从而获取最优的系统响应动作.在音乐搜索领域数据集上的实验结果表明,与随机策略、基于规则和基于信息熵的算法相比,该算法分别缩短了2.24、0.84和0.03个对话轮次,且能够有效提高对话任务完成率.     

融合马尔科夫决策过程与信息熵的对话策略

对话策略是人机对话系统中的重要组成成分,其性能的优劣直接影响对话系统的性能.在面对完全没有数据的冷启动场景时,收集对话数据进行对话策略学习的过程非常复杂和耗时.为在冷启动场景下能够保持良好性能,提出一种融合马尔科夫决策过程与信息熵的对话算法.利用马尔科夫决策过程快速获得下一步最优对话状态,并结合知识库通过引入属性信息熵方法排除多个状态值函数相同的最优状态,从而获取最优的系统响应动作.在音乐搜索领域数据集上的实验结果表明,与随机策略、基于规则和基于信息熵的算法相比,该算法分别缩短了2.24、0.84和0.03个对话轮次,且能够有效提高对话任务完成率.     

Do human factors experts accept the ISO 9241 Part 10—Dialogue Principle—standard?

This paper presents the results of an international questionnaire survey that was developed to analyse how the potential addressees of the ISO 9241 part 10 standard—human factor (HF) experts engaged in the design, evaluation, purchase, and application of software systems—accept the first committee draft of this standard. It reports how HF experts from nine countries evaluated the First Committee Draft of ISO 9241 Part 10. Inquiries were made about whether the standard provides a framework for the design and evaluation of dialogue systems, or whether the subject of the standard is mature enough to be published as an international recommendation. Results indicate a widespread approval among 90 HF experts of the form and content of ISO 9241 Part 10.     

On Synchronous Robotic Networks—Part I: Models, Tasks, and Complexity

This paper proposes a formal model for a network of robotic agents that move and communicate. Building on concepts from distributed computation, robotics, and control theory, we define notions of robotic network, control and communication law, coordination task, and time and communication complexity. We illustrate our model and compute the proposed complexity measures in the example of a network of locally connected agents on a circle that agree upon a direction of motion and pursue their immediate neighbors.