Multi-Relationship Evaluation Design (MRED): An Interactive Test Plan Designer for Advanced and Emerging Technologies

Ground-breaking technologies are developed for use across a broad range of domains such as manufacturing, military, homeland security and automotive industries. These advanced technologies often include intelligent systems or robotic elements. Evaluations are a critical step in the development of these advanced systems. Evaluation events inform the technology developers of specific needs for enhancement, capture end-user feedback, and verify the extent of the technology's functions. Test exercises are an opportunity to showcase the technology's current abilities and limitations and provide data for future test efforts. The objective of this research is to develop the Multi-Relationship Evaluation Design (MRED) methodology, an interactive test plan blueprint generator. MRED collects multiple inputs, processes them interactively with a test designer and outputs evaluation blueprints, specifying key test-plan characteristics. Drawing from the Systems Engineering Paradigm, MRED models a process that had not been modeled before. The MRED model is consistent with the experience of evaluation designers. This method also captures and handles stakeholder preferences so that they can be accommodated in a meaningful way. The result is the MRED methodology that combines practical evaluation design experience with mathematical methods proven in the literature

Speech-to-speech translation to support medical interviews

Projeto de mestrado em Engenharia Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013Este relatório apresenta a criação de um sistema de tradução fala-para-fala. O sistema consiste na captação de voz na forma de sinal áudio que de seguida é interpretado, traduzido e sintetizado para voz. Tendo como entrada um enunciado numa linguagem de origem e como saída um enunciado numa linguagem destino. O sistema implementado tem como âmbito do seu funcionamento o domínio médico, tendo em vista apoiar o diálogo entre médico e utente em linguagens diferentes durante consultas médicas. No caso do presente trabalho, foram escolhidos o português e inglês, sendo possível a tradução fala-para-fala nos dois sentidos. A escolha destas duas línguas resulta sobretudo da disponibilidade de recursos para o desenvolvimento do sistema. Ao longo dos anos tem existido um esforço de pesquisa e desenvolvimento em tecnologia que permite quebrar as barreiras do multilinguismo. Uma dessas tecnologias, com resultados de qualidade crescentemente aceitável, são os sistemas de tradução fala-para-fala. Em geral, estes sistemas são compostos por três componentes: reconhecimento de fala, tradução automática e sintetização de voz. Neste projecto foram implementadas as três componentes. No entanto, uma vez que face às tecnologias disponíveis, a componente de tradução tem um maior impacto no desempenho final do sistema, a esta foi conferida uma maior atenção. Embora nós, como humanos, compreendamos facilmente a linguagem falada, isto é algo extremamente difícil e complexo de um ponto de vista computacional. O objectivo do reconhecimento de fala é abordar esta tarefa computacionalmente através da construção de sistemas que mapeiam um sinal acústico para uma sequência de caracteres. Os modelos actuais para reconhecimento de fala fazem uso de modelos estatísticos. Nestes, a fala é reconhecida através do uso de modelos de linguagem que possibilitam a estimativa das probabilidades para as palavras, independentemente do sinal de entrada, e de um modelo acústico onde as propriedades acústicas da fala estão contempladas. Os modelos actuais de tradução automática, assim como os de reconhecimento de fala, são na sua larga maioria estatísticos. Actualmente os modelos de tradução baseados em unidades frásicas de input são os que obtém os resultados com melhor qualidade. Esta abordagem consiste na tradução de pequenos segmentos de palavras, onde existe uma tradução lexical e um modelo de alinhamento. Os modelos estatísticos fazem uso de textos de duas línguas alinhados, tendo como princípio o facto de que através da frequência de cada segmento de palavras, em relação à outra linguagem, seja obtida uma distribuição probabilística. Deste modo torna-se possível calcular qual a palavra ou conjunto de palavras mais prováveis de ocorrer como tradução para determinado texto que se pretenda traduzir. A sintetização de voz consiste na geração de fala na forma de onda acústica tendo como ponto de partida uma palavra ou uma sequência de palavras. Envolve o processamento de linguagens naturais e processamento de sinal. O primeiro converte o texto numa representação fonética e o último converte essa representação em sinal acústico. Neste documento é apresentado o estado da arte das três áreas envolvidas. São também apresentados os sistemas de tradução fala-para-fala, fazendo ou não uso do domínio médico, e também os processos existentes para a avaliação de cada uma das componentes. Tendo em vista a implementação de um sistema com as diversas componentes, foi necessário efectuar um levantamento da tecnologia existente. O levantamento teve por objectivo a implementação de duas soluções aplicacionais. Uma aplicação disponível pela internet como página web e outra através de uma aplicação móvel, ambas permitindo o reconhecimento de fala, tradução automática e sintetização de voz em ambas as linguagens e direcções. Dois sistemas de reconhecimento de fala foram escolhidos, o Microsoft Speech Platform para a aplicação móvel e o reconhecimento de fala disponível pelo Google nos browsers Google Chrome. O primeiro a ser usado na aplicação móvel e o segundo na aplicação web. O sistema de tradução automática escolhido foi o Moses. Sendo um sistema de tradução estatístico que permite a criação de modelos de tradução diversos, como os modelos baseados em frase e os modelos baseados em fatores. O sistema de sintetização de voz escolhido foi o Microsoft Speech Platform. A aplicação móvel foi desenvolvida para a plataforma iOS da Apple tendo em vista o uso de um telemóvel iPhone. A integração dos componentes pelas diversas arquitecturas foi assegurada pela implementação de web services. O reconhecimento de fala na aplicação web foi desenvolvido recorrendo ao uso da W3C Speech Input API Specifications, onde a programação através de HTML permite a captação de áudio no Google Chrome. Para a implementação do sistema tradução fala-para-fala foi necessário a obtenção de corpora paralelos de forma a se poder treinar os modelos estatísticos, sendo este um dos factores cruciais para o bom desempenho dos componentes. Uma vez que o sistema tem como domínio de aplicação o diálogo médico, corpora neste domínio seria o mais vantajoso. No entanto, a inexistência de tais corpora para o par Inglês-Português levou à aquisição de corpora alternativos. Através de uma experiência exploratória foi abordado o tipo de implementação mais adequado da componente de reconhecimento de fala, tendo como foco o modelo de linguagem. Três experiências foram então conduzidas de forma a decidir entre a aplicação de um modelo de linguagem baseado em regras ou um modelo estatístico. Para implementar um modelo de linguagem baseado em regras foi necessário a criação de um corpus médico que reflectisse um diálogo entre médico e paciente. Para tal, com a ajuda de um médico, criei um diálogo de um caso hipotético de lesão num braço devido a um acidente de carro. Este diálogo teve como base para a sua estruturação a aplicação do processo de anamnesis. A anamnesis consiste numa metodologia médica que através de um conjunto de perguntas chave permite adquirir a informação necessária para a formulação de um diagnóstico médico e decisão sobre o tratamento necessário. O corpus médico foi também transformado num corpus de fala de forma a este ser avaliado ao longo das experiências. Numa primeira experiência foi criada uma gramática básica cuja implementação foi obtida recorrendo à Speech Recognition Grammar Specification de forma a ser usada como modelo de linguagem pela componente de reconhecimento de fala. A segunda experiência tinha como objectivo a criação de uma gramática mais complexa que a primeira. Para tal foi criada uma gramática livre de contexto. Após a criação da gramática livre de contexto esta foi convertida manualmente para uma gramática SRGS. Na terceira experiência foram criados dois modelo de linguagem estatísticos, o primeiro fazendo uso do mesmo corpus que o usado nas experiências anteriores e o segundo composto por 30.000 frases independentes. Obteve-se melhores resultados com o modelo de linguagem estatístico e este ficou como a escolha para a implementação do componente de reconhecimento de fala. No treino da componente de tradução automática foram usados dois modelos estatísticos, baseados em frases e em factores. Pretendeu-se comparar os resultados entre os dois modelos de forma a escolher o modelo mais vantajoso. Para fazer uso do modelo baseado em factores foi necessária a preparação de corpora. Com os corpora já adquiridos foi concretizada a sua anotação para ambas as linguagens. Recorrendo ao LX-Suite e ao CoreNLP, foram criados corpora anotados com lemmas e informação morfossintáctica, com a primeira ferramenta para o português e a última para o inglês. Uma vez que a componente de sintetização de voz permitia uma implementação célere, esta foi implementada recorrendo aos modelos já existentes para ambas as linguagens e disponibilizados pela ferramenta. Por fim, são apresentados os resultados obtidos e a sua avaliação. Tanto a avaliação do sistema de reconhecimento de fala como o de tradução automática demonstraram um desempenho muito competitivo, do nível do estado da arte. A componente de reconhecimento de fala, assim como a componente de tradução automática, obtiveram melhores resultados fazendo-se uso de modelos de linguagem estatístico.This report presents the development of a speech-to-speech translation system. The system consists in the capture of voice as an audio signal that is then interpreted, translated and synthesized to voice for a target language. The three main components of the system, speech recognition, machine translation and speech synthesis, make use of statistical models, such as hidden Markov models. Given the technology available, the machine translation component has a greater impact on the performance of the system, a greater attention has thus been given to it. The system assumes the support to medical interviews between doctor and patient in different languages as its applicational domain. Two application solutions were developed: an online service on a website and a mobile application. This report begins by presenting the general concepts of the relevant areas involved. It proceeds with an overview of the state of the art relating to each area as well as to the methods used for the evaluation of the different components. It provides also an overview of existing technology and the criteria for choosing the tools to be used in the development of the system. It explains the acquisition and creation of the corpora used, and the process of development and integration of the components: speech recognition, machine translation and text-to-speech. Finally, the evaluation results are presented, as well as the final conclusions

Problem solving activities in post-editing and translation from scratch: A multi-method study

Companies and organisations are increasingly using machine translation to improve efficiency and cost-effectiveness, and then edit the machine translated output to create a fluent text that adheres to given text conventions. This procedure is known as post-editing. Translation and post-editing can often be categorised as problem-solving activities. When the translation of a source text unit is not immediately obvious to the translator, or in other words, if there is a hurdle between the source item and the target item, the translation process can be considered problematic. Conversely, if there is no hurdle between the source and target texts, the translation process can be considered a task-solving activity and not a problem-solving activity. This study investigates whether machine translated output influences problem-solving effort in internet research, syntax, and other problem indicators and whether the effort can be linked to expertise. A total of 24 translators (twelve professionals and twelve semi-professionals) produced translations from scratch from English into German, and (monolingually) post-edited machine translation output for this study. The study is part of the CRITT TPR-DB database. The translation and (monolingual) post-editing sessions were recorded with an eye-tracker and a keylogging program. The participants were all given the same six texts (two texts per task). Different approaches were used to identify problematic translation units. First, internet research behaviour was considered as research is a distinct indicator of problematic translation units. Then, the focus was placed on syntactical structures in the MT output that do not adhere to the rules of the target language, as I assumed that they would cause problems in the (monolingual) post-editing tasks that would not occur in the translation from scratch task. Finally, problem indicators were identified via different parameters like Munit, which indicates how often the participants created and modified one translation unit, or the inefficiency (InEff) value of translation units, i.e. the number of produced and deleted tokens divided by the final length of the translation. Finally, the study highlights how these parameters can be used to identify problems in the translation process data using mere keylogging data

Problem solving activities in post-editing and translation from scratch: A multi-method study

Companies and organisations are increasingly using machine translation to improve efficiency and cost-effectiveness, and then edit the machine translated output to create a fluent text that adheres to given text conventions. This procedure is known as post-editing. Translation and post-editing can often be categorised as problem-solving activities. When the translation of a source text unit is not immediately obvious to the translator, or in other words, if there is a hurdle between the source item and the target item, the translation process can be considered problematic. Conversely, if there is no hurdle between the source and target texts, the translation process can be considered a task-solving activity and not a problem-solving activity. This study investigates whether machine translated output influences problem-solving effort in internet research, syntax, and other problem indicators and whether the effort can be linked to expertise. A total of 24 translators (twelve professionals and twelve semi-professionals) produced translations from scratch from English into German, and (monolingually) post-edited machine translation output for this study. The study is part of the CRITT TPR-DB database. The translation and (monolingual) post-editing sessions were recorded with an eye-tracker and a keylogging program. The participants were all given the same six texts (two texts per task). Different approaches were used to identify problematic translation units. First, internet research behaviour was considered as research is a distinct indicator of problematic translation units. Then, the focus was placed on syntactical structures in the MT output that do not adhere to the rules of the target language, as I assumed that they would cause problems in the (monolingual) post-editing tasks that would not occur in the translation from scratch task. Finally, problem indicators were identified via different parameters like Munit, which indicates how often the participants created and modified one translation unit, or the inefficiency (InEff) value of translation units, i.e. the number of produced and deleted tokens divided by the final length of the translation. Finally, the study highlights how these parameters can be used to identify problems in the translation process data using mere keylogging data

Problem solving activities in post-editing and translation from scratch: A multi-method study

Companies and organisations are increasingly using machine translation to improve efficiency and cost-effectiveness, and then edit the machine translated output to create a fluent text that adheres to given text conventions. This procedure is known as post-editing. Translation and post-editing can often be categorised as problem-solving activities. When the translation of a source text unit is not immediately obvious to the translator, or in other words, if there is a hurdle between the source item and the target item, the translation process can be considered problematic. Conversely, if there is no hurdle between the source and target texts, the translation process can be considered a task-solving activity and not a problem-solving activity. This study investigates whether machine translated output influences problem-solving effort in internet research, syntax, and other problem indicators and whether the effort can be linked to expertise. A total of 24 translators (twelve professionals and twelve semi-professionals) produced translations from scratch from English into German, and (monolingually) post-edited machine translation output for this study. The study is part of the CRITT TPR-DB database. The translation and (monolingual) post-editing sessions were recorded with an eye-tracker and a keylogging program. The participants were all given the same six texts (two texts per task). Different approaches were used to identify problematic translation units. First, internet research behaviour was considered as research is a distinct indicator of problematic translation units. Then, the focus was placed on syntactical structures in the MT output that do not adhere to the rules of the target language, as I assumed that they would cause problems in the (monolingual) post-editing tasks that would not occur in the translation from scratch task. Finally, problem indicators were identified via different parameters like Munit, which indicates how often the participants created and modified one translation unit, or the inefficiency (InEff) value of translation units, i.e. the number of produced and deleted tokens divided by the final length of the translation. Finally, the study highlights how these parameters can be used to identify problems in the translation process data using mere keylogging data

Problem solving activities in post-editing and translation from scratch: A multi-method study

Companies and organisations are increasingly using machine translation to improve efficiency and cost-effectiveness, and then edit the machine translated output to create a fluent text that adheres to given text conventions. This procedure is known as post-editing. Translation and post-editing can often be categorised as problem-solving activities. When the translation of a source text unit is not immediately obvious to the translator, or in other words, if there is a hurdle between the source item and the target item, the translation process can be considered problematic. Conversely, if there is no hurdle between the source and target texts, the translation process can be considered a task-solving activity and not a problem-solving activity. This study investigates whether machine translated output influences problem-solving effort in internet research, syntax, and other problem indicators and whether the effort can be linked to expertise. A total of 24 translators (twelve professionals and twelve semi-professionals) produced translations from scratch from English into German, and (monolingually) post-edited machine translation output for this study. The study is part of the CRITT TPR-DB database. The translation and (monolingual) post-editing sessions were recorded with an eye-tracker and a keylogging program. The participants were all given the same six texts (two texts per task). Different approaches were used to identify problematic translation units. First, internet research behaviour was considered as research is a distinct indicator of problematic translation units. Then, the focus was placed on syntactical structures in the MT output that do not adhere to the rules of the target language, as I assumed that they would cause problems in the (monolingual) post-editing tasks that would not occur in the translation from scratch task. Finally, problem indicators were identified via different parameters like Munit, which indicates how often the participants created and modified one translation unit, or the inefficiency (InEff) value of translation units, i.e. the number of produced and deleted tokens divided by the final length of the translation. Finally, the study highlights how these parameters can be used to identify problems in the translation process data using mere keylogging data

Problem solving activities in post-editing and translation from scratch: A multi-method study

Companies and organisations are increasingly using machine translation to improve efficiency and cost-effectiveness, and then edit the machine translated output to create a fluent text that adheres to given text conventions. This procedure is known as post-editing. Translation and post-editing can often be categorised as problem-solving activities. When the translation of a source text unit is not immediately obvious to the translator, or in other words, if there is a hurdle between the source item and the target item, the translation process can be considered problematic. Conversely, if there is no hurdle between the source and target texts, the translation process can be considered a task-solving activity and not a problem-solving activity. This study investigates whether machine translated output influences problem-solving effort in internet research, syntax, and other problem indicators and whether the effort can be linked to expertise. A total of 24 translators (twelve professionals and twelve semi-professionals) produced translations from scratch from English into German, and (monolingually) post-edited machine translation output for this study. The study is part of the CRITT TPR-DB database. The translation and (monolingual) post-editing sessions were recorded with an eye-tracker and a keylogging program. The participants were all given the same six texts (two texts per task). Different approaches were used to identify problematic translation units. First, internet research behaviour was considered as research is a distinct indicator of problematic translation units. Then, the focus was placed on syntactical structures in the MT output that do not adhere to the rules of the target language, as I assumed that they would cause problems in the (monolingual) post-editing tasks that would not occur in the translation from scratch task. Finally, problem indicators were identified via different parameters like Munit, which indicates how often the participants created and modified one translation unit, or the inefficiency (InEff) value of translation units, i.e. the number of produced and deleted tokens divided by the final length of the translation. Finally, the study highlights how these parameters can be used to identify problems in the translation process data using mere keylogging data

Problem solving activities in post-editing and translation from scratch: A multi-method study

Companies and organisations are increasingly using machine translation to improve efficiency and cost-effectiveness, and then edit the machine translated output to create a fluent text that adheres to given text conventions. This procedure is known as post-editing. Translation and post-editing can often be categorised as problem-solving activities. When the translation of a source text unit is not immediately obvious to the translator, or in other words, if there is a hurdle between the source item and the target item, the translation process can be considered problematic. Conversely, if there is no hurdle between the source and target texts, the translation process can be considered a task-solving activity and not a problem-solving activity. This study investigates whether machine translated output influences problem-solving effort in internet research, syntax, and other problem indicators and whether the effort can be linked to expertise. A total of 24 translators (twelve professionals and twelve semi-professionals) produced translations from scratch from English into German, and (monolingually) post-edited machine translation output for this study. The study is part of the CRITT TPR-DB database. The translation and (monolingual) post-editing sessions were recorded with an eye-tracker and a keylogging program. The participants were all given the same six texts (two texts per task). Different approaches were used to identify problematic translation units. First, internet research behaviour was considered as research is a distinct indicator of problematic translation units. Then, the focus was placed on syntactical structures in the MT output that do not adhere to the rules of the target language, as I assumed that they would cause problems in the (monolingual) post-editing tasks that would not occur in the translation from scratch task. Finally, problem indicators were identified via different parameters like Munit, which indicates how often the participants created and modified one translation unit, or the inefficiency (InEff) value of translation units, i.e. the number of produced and deleted tokens divided by the final length of the translation. Finally, the study highlights how these parameters can be used to identify problems in the translation process data using mere keylogging data

Problem solving activities in post-editing and translation from scratch: A multi-method study

Companies and organisations are increasingly using machine translation to improve efficiency and cost-effectiveness, and then edit the machine translated output to create a fluent text that adheres to given text conventions. This procedure is known as post-editing. Translation and post-editing can often be categorised as problem-solving activities. When the translation of a source text unit is not immediately obvious to the translator, or in other words, if there is a hurdle between the source item and the target item, the translation process can be considered problematic. Conversely, if there is no hurdle between the source and target texts, the translation process can be considered a task-solving activity and not a problem-solving activity. This study investigates whether machine translated output influences problem-solving effort in internet research, syntax, and other problem indicators and whether the effort can be linked to expertise. A total of 24 translators (twelve professionals and twelve semi-professionals) produced translations from scratch from English into German, and (monolingually) post-edited machine translation output for this study. The study is part of the CRITT TPR-DB database. The translation and (monolingual) post-editing sessions were recorded with an eye-tracker and a keylogging program. The participants were all given the same six texts (two texts per task). Different approaches were used to identify problematic translation units. First, internet research behaviour was considered as research is a distinct indicator of problematic translation units. Then, the focus was placed on syntactical structures in the MT output that do not adhere to the rules of the target language, as I assumed that they would cause problems in the (monolingual) post-editing tasks that would not occur in the translation from scratch task. Finally, problem indicators were identified via different parameters like Munit, which indicates how often the participants created and modified one translation unit, or the inefficiency (InEff) value of translation units, i.e. the number of produced and deleted tokens divided by the final length of the translation. Finally, the study highlights how these parameters can be used to identify problems in the translation process data using mere keylogging data