图像理解

图像理解是从图像中自动提取信息的一门科学，该学科的基本技术包括图像组成模型化、图像建立和数据可视化。对于开发基于输入图像解释来做出决策的人工智能系统来说图像理解是一个重要环节。该文就知识在航空图像理解系统中的作用进行了探讨，主要介绍了自动理解中的知识表示和控制结构，并通过一个机场的例子，说明图像解释中通用知识的应用，进一步强调图像理解中知识运用的重要性。     

智能答疑系统中语义网的研究与应用

目前答疑系统大多以关键词匹配为基础，而智能答疑系统是在此基础之上，增加了对语义的理解。文中讨论了专业领域义原的抽取、语义网和语义树的建立，并且就语义关系在智能答疑系统中的角色做了阐述。最后，实现了一个原型系统，通过增加对语义关系的理解，取得了比较好的测试结果。     

集群计算系统的模型结构

对集群计算环境进行了抽象的分析和概述，并建立了一个集群计算系统的模型，该模型为理解、分析和评价一个实际的集群系统提供了有力的工具。     

C++程序理解辅助系统*

本文介绍了一个针对c++语言的程序理解辅助系统HOOPUS(hypertext—basedobject—oriented program understanding support system)．该系统基于程序结构分析技术，以易于理解的形式显示程序中的信息，辅助理解c++程序的功能与结构．另外，它还利用hypertext、多维表现、导航与定位等技术对理解过程提供支持．     

基于自然语言计算模型的汉语理论解系统

本文首先给出了一种自然语言计算模型，该模型把自然语言交流过程划分为三个层次：语言形式，表层语义和深层语义，从而将自然语言理解抽象为一个复合函数ＵＰ。依据这个模型，我们设计了一个汉语理解系统，这个系统具有良好的扩展性和可移植性，该系统采用汉语语义结构文法来分析汉语句子，反语法分析和语义分析有机地结合在一卢。文中形式定义了词语的深层语义以及深层语义的基本运算，给出了分析器，理解器以及生成器的算法。     

基于知识的机械图纸理解系统

本文提出了一个基于知识的机械图纸理解系统（ＭＤＩＳ），它运用了大量由规则和过程表示的机械图纸识别与理解知识，并采用一个自底向上与自顶向下相结合的混合推理机制，较好地实现了对机械图纸的识别与理解。     

架起CPU与内存桥梁——缓存详解（上）

为真正理解缓存在系统中扮演的重要角色，我们必须理解缓存与CPU、内存子系统在处理一个任务时，相互之间的联系。     

C＋＋程序理解辅助系统

本文介绍了一个针对Ｃ＋＋语言的程序理解辅助系统ＨＯＯＰＵＳ（ｈｙｐｅｒｔｅｘｔ－ｂａｓｅｄｏｂｊｅｃｔ－ｏｒｉｅｎｅｄ ｐｒｏｇｒａｍ ｕｎｄｅｒｓｔａｎｄｉｎｇ ｓｕｐｐｏｒｔ ｓｙｓｔｅｍ），该系统基于程序结构分析技术，以易于程序中的信息，辅助理解Ｃ＋＋程序的功能与结构，另外，还利用ｈｙｐｅｒｔｅｘｔ、多维表现、导航与定位等技术对理解过程提供支持。     

别把钥匙锁在家里——挖掘EFS文件私钥的藏身之处

从Windows 2000开始Windows引入了加密文件系统（EFS）。EFS是一个很复杂的系统，对于广大用户来说，理解它的具体加密、解密和恢复是一个艰难的过程。我们下面会用一种比较利于理解的方式来简单地找到用户密钥在Widows XP Professional中存储的位置．帮助大家更形象地理解FFS．同时能更安全地使用EFS功能。     

基于本体的智能答疑系统的研究与实现

自动答疑是现代远程教学系统中一个重要组成部分，然而目前的自动答疑系统大多以关键词匹配为基础，忽视了关键粤本身所含的语义信息、查询效率和结果不能满足用户的需要。提出了一个基于本体的智能答疑系统，利用本体中概念规范、语义关系明确的特点，将用户的问题查询请求进行语义上的扩展，增加了对语义信息的理解，有效地提高了查询的准确性。     

Testing the performance of spoken dialogue systems by means of an artificially simulated user

This paper proposes a new technique to test the performance of spoken dialogue systems by artificially simulating the behaviour of three types of user (very cooperative, cooperative and not very cooperative) interacting with a system by means of spoken dialogues. Experiments using the technique were carried out to test the performance of a previously developed dialogue system designed for the fast-food domain and working with two kinds of language model for automatic speech recognition: one based on 17 prompt-dependent language models, and the other based on one prompt-independent language model. The use of the simulated user enables the identification of problems relating to the speech recognition, spoken language understanding, and dialogue management components of the system. In particular, in these experiments problems were encountered with the recognition and understanding of postal codes and addresses and with the lengthy sequences of repetitive confirmation turns required to correct these errors. By employing a simulated user in a range of different experimental conditions sufficient data can be generated to support a systematic analysis of potential problems and to enable fine-grained tuning of the system.     

Using knowledge of misunderstandings to increase the robustness of spoken dialogue systems

This paper proposes a new technique to increase the robustness of spoken dialogue systems employing an automatic procedure that aims to correct frames incorrectly generated by the system’s component that deals with spoken language understanding. To do this the technique carries out a training that takes into account knowledge of previous system misunderstandings. The correction is transparent for the user as he is not aware of some mistakes made by the speech recogniser and thus interaction with the system can proceed more naturally. Experiments have been carried out using two spoken dialogue systems previously developed in our lab: Saplen and Viajero, which employ prompt-dependent and prompt-independent language models for speech recognition. The results obtained from 10,000 simulated dialogues show that the technique improves the performance of the two systems for both kinds of language modelling, especially for the prompt-independent language model. Using this type of model the Saplen system increases sentence understanding by 19.54%, task completion by 26.25%, word accuracy by 7.53%, and implicit recovery of speech recognition errors by 20.3%, whereas for the Viajero system these figures increase by 14.93%, 18.06%, 6.98% and 15.63%, respectively.     

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.     

Combining language models in the input interface of a spoken dialogue system

This paper presents a new technique to enhance the performance of the input interface of spoken dialogue systems based on a procedure that combines during speech recognition the advantages of using prompt-dependent language models with those of using a language model independent of the prompts generated by the dialogue system. The technique proposes to create a new speech recognizer, termed contextual speech recognizer, that uses a prompt-independent language model to allow recognizing any kind of sentence permitted in the application domain, and at the same time, uses contextual information (in the form of prompt-dependent language models) to take into account that some sentences are more likely to be uttered than others at a particular moment of the dialogue. The experiments show the technique allows enhancing clearly the performance of the input interface of a previously developed dialogue system based exclusively on prompt-dependent language models. But most important, in comparison with a standard speech recognizer that uses just one prompt-independent language model without contextual information, the proposed recognizer allows increasing the word accuracy and sentence understanding rates by 4.09% and 4.19% absolute, respectively. These scores are slightly better than those obtained using linear interpolation of the prompt-independent and prompt-dependent language models used in the experiments.     

基于上下文信息的口语意图检测方法

近年来,随着人工智能的发展与智能设备的普及,人机智能对话技术得到了广泛的关注。口语语义理解是口语对话系统中的一项重要任务,而口语意图检测是口语语义理解中的关键环节。由于多轮对话中存在语义缺失、框架表示以及意图转换等复杂的语言现象,因此面向多轮对话的意图检测任务十分具有挑战性。为了解决上述难题,文中提出了基于门控机制的信息共享网络,充分利用了多轮对话中的上下文信息来提升检测性能。具体而言,首先结合字音特征构建当前轮文本和上下文文本的初始表示,以减小语音识别错误对语义表示的影响;其次,使用基于层级化注意力机制的语义编码器得到当前轮和上下文文本的深层语义表示,包含由字到句再到多轮文本的多级语义信息;最后,通过在多任务学习框架中引入门控机制来构建基于门控机制的信息共享网络,使用上下文语义信息辅助当前轮文本的意图检测。实验结果表明,所提方法能够高效地利用上下文信息来提升口语意图检测效果,在全国知识图谱与语义计算大会(CCKS2018)技术评测任务2的数据集上达到了88.1%的准确率(Acc值)和88.0%的综合正确率(F1值),相比于已有的方法显著提升了性能。     

Designing habitable dialogues for speech-based interaction with computers

Habitability refers to the match between the language people employ when using a computer system and the language that the system can accept. In this paper, the concept of “habitability” is explored in relation to the design of dialogues for speech-based systems. Two studies investigating the role of habitability in speech systems for banking applications are reported. The first study employed a speech-driven automated teller machine (ATM), using a visual display to indicate available vocabulary. Users made several distinct types of error with this system, indicating that habitability in speech systems cannot be achieved simply by displaying the input language. The second study employed a speech input/speech output home banking application, in which system constraints were indicated by either a spoken menu of words or a “query-style” prompt (e.g. “what service do you require?”). Between-subjects comparisons of these two conditions confirmed that the “menu-style” dialogue was rated as more habitable than the “query-style”. It also led to fewer errors, and was rated as easier to use, suggesting that habitability is a key issue in speech system usability. Comparison with the results of the first study suggests that for speech input, spoken menu prompts may be more habitable than similar menus shown on a visual display. The implications of these results to system design are discussed, and some initial dialogue design recommendations are presented.     

A robust system for human-machine dialogue in telephony-based applications

This paper presents a real-time system for human-machine spoken dialogue on the telephone in task-oriented domains. The system has been tested in a large trial with inexperienced users and it has proved robust enough to allow spontaneous interactions even for people with poor recognition performance. The robust behaviour of the system has been achieved by combining the use of specific language models during the recognition phase of analysis, the tolerance toward spontaneous speech phenomena, the activity of a robust parser, and the use of pragmatic-based dialogue knowledge. This integration of the different modules allows the system to deal with partial or total breakdowns at other levels of analysis. We report the field trial data of the system with respect to speech recognition metrics of word accuracy and sentence understanding rate, time-to-completion, time-to-acquisition of crucial parameters, and degree of success of the interactions in providing the speakers with the information they required. The evaluation data show that most of the subjects were able to interact fruitfully with the system. These results suggest that the design choices made to achieve robust behaviour are a promising way to create usable spoken language telephone systems.     

On the dynamic adaptation of language models based on dialogue information

We present an approach to adapt dynamically the language models (LMs) used by a speech recognizer that is part of a spoken dialogue system. We have developed a grammar generation strategy that automatically adapts the LMs using the semantic information that the user provides (represented as dialogue concepts), together with the information regarding the intentions of the speaker (inferred by the dialogue manager, and represented as dialogue goals). We carry out the adaptation as a linear interpolation between a background LM, and one or more of the LMs associated to the dialogue elements (concepts or goals) addressed by the user. The interpolation weights between those models are automatically estimated on each dialogue turn, using measures such as the posterior probabilities of concepts and goals, estimated as part of the inference procedure to determine the actions to be carried out. We propose two approaches to handle the LMs related to concepts and goals. Whereas in the first one we estimate a LM for each one of them, in the second one we apply several clustering strategies to group together those elements that share some common properties, and estimate a LM for each cluster. Our evaluation shows how the system can estimate a dynamic model adapted to each dialogue turn, which helps to significantly improve the performance of the speech recognition, which leads to an improvement in both the language understanding and the dialogue management tasks.     

Evaluation of Methods for the Determination of Factors Inducing Fatigue in Man at Work

The aim of the study was to determine the influence of textual feedback on the content and outcome of spoken interaction with a natural language dialogue system. More specifically, the assumption that textual feedback could disrupt spoken interaction was tested in a human–computer dialogue situation. In total, 48 adult participants, familiar with the system, had to find restaurants based on simple or difficult scenarios using a real natural language service system in a speech-only (phone), speech plus textual dialogue history (multimodal) or text-only (web) modality. The linguistic contents of the dialogues differed as a function of modality, but were similar whether the textual feedback was included in the spoken condition or not. These results add to burgeoning research efforts on multimodal feedback, in suggesting that textual feedback may have little or no detrimental effect on information searching with a real system.

Statement of Relevance: The results suggest that adding textual feedback to interfaces for human–computer dialogue could enhance spoken interaction rather than create interference. The literature currently suggests that adding textual feedback to tasks that depend on the visual sense benefits human–computer interaction. The addition of textual output when the spoken modality is heavily taxed by the task was investigated.     

限定领域口语对话系统中超出领域话语的协处理方法

领域外话语的开放性、口语化以及表达多样性,使得现有的限定领域口语对话系统不能很好地处理超出领域话语。该文提出了一种限定领域口语对话系统协处理方案,基于人工智能标记语言AIML,设计一套理解开放语义用户话语的理解模板,并对未匹配话语基于话语相似度进行理解模板分类,进而采用扩展有限状态自动机处理模式,结合对话流程上下文的状态及信息,实现理解模板到应答模板的转换,改变了单纯模板匹配方法在对话流程控制方面的相对缺失。中文手机导购领域的测试表明,该文所提出的协处理方法能有效地辅助口语对话系统完成限定领域完整对话流程,得到更好的用户满意度。