Automatic translation of formal data specifications to voice data-input applications.

This thesis introduces a complete solution for automatic translation of formal data specifications to voice data-input applications. The objective of the research is to automatically generate applications for inputting data through speech from specifications of the structure of the data. The formal data specifications are XML DTDs. A new formalization called Grammar-DTD (G-DTD) is introduced as an extended DTD that contains grammars to describe valid values of the DTD elements and attributes. G-DTDs facilitate the automatic generation of Voice XML applications that correspond to the original DTD structure. The development of the automatic application-generator included identifying constraints on the G-DTD to ensure a feasible translation, using predicate calculus to build a knowledge base of inference rules that describes the mapping procedure, and writing an algorithm for the automatic translation based on the inference rules.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .H355. Source: Masters Abstracts International, Volume: 45-01, page: 0354. Thesis (M.Sc.)--University of Windsor (Canada), 2006

An interactive multimedia continuously learning helpdesk system : (when Hal met Sally)

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 65-72).by Marion L. Groh.S.B.and M.Eng

A study of the use of natural language processing for conversational agents

Language is a mark of humanity and conscience, with the conversation (or dialogue) as one of the most fundamental manners of communication that we learn as children. Therefore one way to make a computer more attractive for interaction with users is through the use of natural language. Among the systems with some degree of language capabilities developed, the Eliza chatterbot is probably the first with a focus on dialogue. In order to make the interaction more interesting and useful to the user there are other approaches besides chatterbots, like conversational agents. These agents generally have, to some degree, properties like: a body (with cognitive states, including beliefs, desires and intentions or objectives); an interactive incorporation in the real or virtual world (including perception of events, communication, ability to manipulate the world and communicate with others); and behavior similar to a human (including affective abilities). This type of agents has been called by several terms, including animated agents or embedded conversational agents (ECA). A dialogue system has six basic components. (1) The speech recognition component is responsible for translating the user’s speech into text. (2) The Natural Language Understanding component produces a semantic representation suitable for dialogues, usually using grammars and ontologies. (3) The Task Manager chooses the concepts to be expressed to the user. (4) The Natural Language Generation component defines how to express these concepts in words. (5) The dialog manager controls the structure of the dialogue. (6) The synthesizer is responsible for translating the agents answer into speech. However, there is no consensus about the necessary resources for developing conversational agents and the difficulties involved (especially in resource-poor languages). This work focuses on the influence of natural language components (dialogue understander and manager) and analyses, in particular the use of parsing systems as part of developing conversational agents with more flexible language capabilities. This work analyses what kind of parsing resources contributes to conversational agents and discusses how to develop them targeting Portuguese, which is a resource-poor language. To do so we analyze approaches to the understanding of natural language, and identify parsing approaches that offer good performance, based on which we develop a prototype to evaluate the impact of using a parser in a conversational agent.linguagem é uma marca da humanidade e da consciência, sendo a conversação (ou diálogo) uma das maneiras de comunicacão mais fundamentais que aprendemos quando crianças. Por isso uma forma de fazer um computador mais atrativo para interação com usuários é usando linguagem natural. Dos sistemas com algum grau de capacidade de linguagem desenvolvidos, o chatterbot Eliza é, provavelmente, o primeiro sistema com foco em diálogo. Com o objetivo de tornar a interação mais interessante e útil para o usuário há outras aplicações alem de chatterbots, como agentes conversacionais. Estes agentes geralmente possuem, em algum grau, propriedades como: corpo (com estados cognitivos, incluindo crenças, desejos e intenções ou objetivos); incorporação interativa no mundo real ou virtual (incluindo percepções de eventos, comunicação, habilidade de manipular o mundo e comunicar com outros agentes); e comportamento similar ao humano (incluindo habilidades afetivas). Este tipo de agente tem sido chamado de diversos nomes como agentes animados ou agentes conversacionais incorporados. Um sistema de diálogo possui seis componentes básicos. (1) O componente de reconhecimento de fala que é responsável por traduzir a fala do usuário em texto. (2) O componente de entendimento de linguagem natural que produz uma representação semântica adequada para diálogos, normalmente utilizando gramáticas e ontologias. (3) O gerenciador de tarefa que escolhe os conceitos a serem expressos ao usuário. (4) O componente de geração de linguagem natural que define como expressar estes conceitos em palavras. (5) O gerenciador de diálogo controla a estrutura do diálogo. (6) O sintetizador de voz é responsável por traduzir a resposta do agente em fala. No entanto, não há consenso sobre os recursos necessários para desenvolver agentes conversacionais e a dificuldade envolvida nisso (especialmente em línguas com poucos recursos disponíveis). Este trabalho foca na influência dos componentes de linguagem natural (entendimento e gerência de diálogo) e analisa em especial o uso de sistemas de análise sintática (parser) como parte do desenvolvimento de agentes conversacionais com habilidades de linguagem mais flexível. Este trabalho analisa quais os recursos do analisador sintático contribuem para agentes conversacionais e aborda como os desenvolver, tendo como língua alvo o português (uma língua com poucos recursos disponíveis). Para isto, analisamos as abordagens de entendimento de linguagem natural e identificamos as abordagens de análise sintática que oferecem um bom desempenho. Baseados nesta análise, desenvolvemos um protótipo para avaliar o impacto do uso de analisador sintático em um agente conversacional

Analysis and Design of Speech-Recognition Grammars

Currently, most commercial speech-enabled products are constructed using grammar-based technology. Grammar design is a critical issue for good recognition accuracy. Two methods are commonly used for creating grammars: 1) to generate them automatically from a large corpus of input data which is very costly to acquire, or 2) to construct them using an iterative process involving manual design, followed by testing with end-user speech input. This is a time-consuming and very expensive process requiring expert knowledge of language design, as well as the application area. Another hurdle to the creation and use of speech-enabled applications is that expertise is also required to integrate the speech capability with the application code and to deploy the application for wide-scale use. An alternative approach, which we propose, is 1) to construct them using the iterative process described above, but to replace end-user testing by analysis of the recognition grammars using a set of grammar metrics which have been shown to be good indicators of recognition accuracy, 2) to improve recognition accuracy in the design process by encoding semantic constraints in the syntax rules of the grammar, 3) to augment the above process by generating recognition grammars automatically from specifications of the application, and 4) to use tools for creating speech-enabled applications together with an architecture for their deployment which enables expert users, as well as users who do not have expertise in language processing, to easily build speech applications and add them to the web

Learning the language of apps

To explore the functionality of an app, automated test generators systematically identify and interact with its user interface (UI) elements. A key challenge is to synthesize inputs which effectively and efficiently cover app behavior. To do so, a test generator has to choose which elements to interact with but, which interactions to do on each element and which input values to type. In summary, to better test apps, a test generator should know the app's language, that is, the language of its graphical interactions and the language of its textual inputs. In this work, we show how a test generator can learn the language of apps and how this knowledge is modeled to create tests. We demonstrate how to learn the language of the graphical input prior to testing by combining machine learning and static analysis, and how to refine this knowledge during testing using reinforcement learning. In our experiments, statically learned models resulted in 50\% less ineffective actions an average increase in test (code) coverage of 19%, while refining these through reinforcement learning resulted in an additional test (code) coverage of up to 20%. We learn the language of textual inputs, by identifying the semantics of input fields in the UI and querying the web for real-world values. In our experiments, real-world values increase test (code) coverage ~10%; Finally, we show how to use context-free grammars to integrate both languages into a single representation (UI grammar), giving back control to the user. This representation can then be: mined from existing tests, associated to the app source code, and used to produce new tests. 82% test cases produced by fuzzing our UI grammar can reach a UI element within the app and 70% of them can reach a specific code location.Automatisierte Testgeneratoren identifizieren systematisch Elemente der Benutzeroberfläche und interagieren mit ihnen, um die Funktionalität einer App zu erkunden. Eine wichtige Herausforderung besteht darin, Eingaben zu synthetisieren, die das App-Verhalten effektiv und effizient abdecken. Dazu muss ein Testgenerator auswählen, mit welchen Elementen interagiert werden soll, welche Interaktionen jedoch für jedes Element ausgeführt werden sollen und welche Eingabewerte eingegeben werden sollen. Um Apps besser testen zu können, sollte ein Testgenerator die Sprache der App kennen, dh die Sprache ihrer grafischen Interaktionen und die Sprache ihrer Texteingaben. In dieser Arbeit zeigen wir, wie ein Testgenerator die Sprache von Apps lernen kann und wie dieses Wissen modelliert wird, um Tests zu erstellen. Wir zeigen, wie die Sprache der grafischen Eingabe lernen vor dem Testen durch maschinelles Lernen und statische Analyse kombiniert und wie dieses Wissen weiter verfeinern beim Testen Verstärkung Lernen verwenden. In unseren Experimenten führten statisch erlernte Modelle zu 50% weniger ineffektiven Aktionen, was einer durchschnittlichen Erhöhung der Testabdeckung (Code) von 19% entspricht, während die Verfeinerung dieser durch verstärkendes Lernen zu einer zusätzlichen Testabdeckung (Code) von bis zu 20% führte. Wir lernen die Sprache der Texteingaben, indem wir die Semantik der Eingabefelder in der Benutzeroberfläche identifizieren und das Web nach realen Werten abfragen. In unseren Experimenten erhöhen reale Werte die Testabdeckung (Code) um ca. 10%; Schließlich zeigen wir, wie kontextfreien Grammatiken verwenden beide Sprachen in einer einzigen Darstellung (UI Grammatik) zu integrieren, wieder die Kontrolle an den Benutzer zu geben. Diese Darstellung kann dann: aus vorhandenen Tests gewonnen, dem App-Quellcode zugeordnet und zur Erstellung neuer Tests verwendet werden. 82% Testfälle, die durch Fuzzing unserer UI-Grammatik erstellt wurden, können ein UI-Element in der App erreichen, und 70% von ihnen können einen bestimmten Code-Speicherort erreichen

Programming Language Techniques for Natural Language Applications

It is easy to imagine machines that can communicate in natural language. Constructing such machines is more difficult. The aim of this thesis is to demonstrate how declarative grammar formalisms that distinguish between abstract and concrete syntax make it easier to develop natural language applications. We describe how the type-theorectical grammar formalism Grammatical Framework (GF) can be used as a high-level language for natural language applications. By taking advantage of techniques from the field of programming language implementation, we can use GF grammars to perform portable and efficient parsing and linearization, generate speech recognition language models, implement multimodal fusion and fission, generate support code for abstract syntax transformations, generate dialogue managers, and implement speech translators and web-based syntax-aware editors. By generating application components from a declarative grammar, we can reduce duplicated work, ensure consistency, make it easier to build multilingual systems, improve linguistic quality, enable re-use across system domains, and make systems more portable