Unsupervised task-oriented dialogue modeling with slot entity memory network = 비지도 목적 지향 대화 모델링을 위한 슬롯 개체 메모리 네트워크

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In this thesis neural network based model for unsupervised modeling of task-oriented dialogue data is proposed. Task-oriented dialogue system recently gained a lot of interest from both academic and industrial areas for its potential for being natural and intuitive interface for applications based on artificial intelligence. However, traditional approaches for modeling task-oriented dialogue had some restrictions concerning with expressiveness of model and scalable training with large amount of data. We propose Slot Entity Memory Network model which is a generative model based on neural networks. Being sufficiently expressive to cope with complex and diverse natural language utterances using neural networks, it can be trained without any human annotated labels on task-oriented dialogues. By incorporating task-oriented dialogue structure in neural network model, Slot Entity Memory Network can help system developers to understand what the model had learned from data. We train the proposed model with CamRest676 dataset and report quantitative and qualitative results with analysis of dialogue structure that model has learned.
Advisors
Lee, Soo-Youngresearcher이수영researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2017.2,[iv, 38 p. :]

Keywords

Deep Neural Network; Task-oriented Dialogue; Dialogue Modeling; Unsupervised Learning; Generative Model; 심화학습; 목적 지향 대화; 대화모델; 비지도 학습; 생성 모델

URI
http://hdl.handle.net/10203/243341
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675450&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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