Title page for etd-0616118-181354


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URN etd-0616118-181354
Author Hao-Yi Wang
Author's Email Address ms0407954@gmail.com
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Department Information Management
Year 2017
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title The Impacts of Image Contexts on Dialogue Systems
Date of Defense 2018-06-25
Page Count 61
Keyword
  • , Dialogue
  • Convolutional neural networks
  • Recurrent neural networks
  • Image recognition
  • Natural language
  • Neural networks
  • Machine learning
  • Abstract Chatting with machines is not only possible but also more and more common in our lives these days. With the approach, we can execute commands and obtain companionship and entertainment through interacting with the machines. In the past, most dialogue systems only used existing replies based on the instructions the machines received. However, it is unlikely for people to feel the vitality of the machine. People still regard their chatting partners are computer systems. In order to develop a more realistic dialogue system, this study adopts a deep neural network to train the machines to make more lifestyle-oriented dialogues. People’s common dialogues include not only simple question-answering problems and the answers are not just as short as a noun or a yes-or-no answer. There are also many diverse and interesting responses. Moreover, suitable communicative responses among the conversationalists depend not only on the contents, but also on the environmental contexts. This study develops a dialogue system to take into account the two essential factors, and uses the TV series as the training datasets. These datasets contain not only conversational contents but also video frames which represent the contexts and the situations when the conversations occur.
      To explore the effect of the image context on the utterance in a dialogue, this work uses a deep neural network model, combining a convolutional neural network (which works well in image recognition) with a recurrent neural network (which achieves excellent performance in natural language). It aims to develop a dialogue system to consider both images and utterances, and to find better ways to evaluate the dialogue system’s responses including defining a quantitative measurement and designing a questionnaire to verify the models learnt from the datasets.
    Advisory Committee
  • Keng-Pei Lin - chair
  • Han-Wei Hsiao - co-chair
  • Wei-Po Lee - advisor
  • Files
  • etd-0616118-181354.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2018-07-17

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