Improving Dialog Systems Using Knowledge Graph Embeddings

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Creator: 

Carignan, Brian

Date: 

2018

Abstract: 

Dialog systems are systems or applications intended to converse with a human user. Recent dialog systems have employed the sequence-to-sequence framework to treat conversation as a translation problem, translating from question to answer in an open-domain. Knowledge graph embedding started as a way to scale question answering to a large, open-domain dataset without the use of hand-crafted rules. This thesis seeks to connect the two, by converting knowledge graph embeddings to word embeddings and evaluating the resulting dialog models.

Subject: 

Computer Science

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Computer Science: 
M.C.S.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Computer Science

Parent Collection: 

Theses and Dissertations

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