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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72240

    Title: A Rapid Deep Learning Model for Goal-Oriented Dialog
    Authors: 施庫瑪;Pradhan,Sipun Kumar
    Contributors: 資訊工程學系
    Keywords: 問題問答;記憶神經網路;長期記憶元件;Question Answering;Memory neural networks;Long-term memory component
    Date: 2016-08-18
    Issue Date: 2016-10-13 14:34:21 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 摘要

    ;Open-domain Question Answering (QA) systems aim at providing the exact answer(s) to questions formulated in natural language, without restriction of domain. My research goal in this thesis is to develop learning models that can automatically induce new facts without having to be re-trained, in particular its structure and meaning in order to solve multiple Open-domain QA tasks. The main advantage of this framework is that it requires little feature engineering and domain specificity whilst matching or surpassing state-of-the-art results. Furthermore, it can easily be trained to be used with any kind of Open-domain QA.

    I investigate a new class of learning models called memory neural networks. Memory neural networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the goal of using it for prediction. I investigate these models in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base, and the output is a textual response. Finally, I show that an end-to-end dialog system based on memory neural networks can reach promising and learn to perform non-trivial operations. I confirm those results by comparing my system to various well-crafted baseline Datasets and future work is discussed.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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