具擷取及萃取能力的摘要模型

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > Electronic Thesis & Dissertation > Item 987654321/81285

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81285

Title:	具擷取及萃取能力的摘要模型
Authors:	陳俞琇;Chen, Yu-Xiu
Contributors:	資訊管理學系
Keywords:	自然語言處理;萃取式摘要;注意力機制;Transformer;複製機制
Date:	2019-07-19
Issue Date:	2019-09-03 15:42:40 (UTC+8)
Publisher:	國立中央大學
Abstract:	自然語言處理的模型由於需要準備字典給模型做挑選，因此衍生出 Out Of Vocabulary(OOV) 這個問題，是指句子裡面有不存在於字典的用詞，過往有人嘗試在 Recurrent Neural Networks(RNN) 上加入複製機制，以改善這個問題。但 Transformer 是自然語言處理的新模型，不若過往的 RNN 或 Convolutional Neural Networks(CNN) 已經有許多改善機制，因此本研究將針對 Transformer 進行改良，添加額外的輸入和輸出的相互注意力，來完成複製機制的概念，讓 Transformer 能有更佳的表現。;In natural language processing, Out of Vocabulary(OOV) has always been a issue. It limits the performance of summarization model. Past study resolve this problem by adding copy mechanism to Recurrent Neural Networks(RNN). However, resent study discover a new model – Transformer which outperforms RNN in many categories. So, this work will improve Transformer model by adding copy mechanism in order to enhance the relation of model’s input and output result..
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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