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論文名稱(外文):A chatbot with personality traits
外文關鍵詞:deep neural networknatural language processingXLNetcapsule networkself-attention mechanismrefined highwaychatbotmix of experts.
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In recent years, with deep learning and natural language processing, many deep neural network models have been developed for prediction or classification, and chatbots have also applied many related technologies. One of the difficulties that needs to be faced is to give chatbots personality. In order to achieve this goal, many methods have been developed, such as using generative chatbots to add personality vectors when generating text, to understand the generated syntax and semantics, or to directly use data sets with personality for model training. This paper is based on retrieval chatbots and automatic personality trait recognition to achieve personalized chatbot tasks, and proposes three personality matching calculation methods and uses stop words, punctuation marks, auxiliary words to train automatic personality trait recognition models and three models of automatic personality Trait Identification Framework. In the calculation of personality matching, absolute value, root mean square and cosine similarity are used respectively. We experimentally demonstrate that training with stopwords, punctuation, and auxiliary word features is effective. In the three proposed personality trait recognition architectures, the XLNet architecture with pre-trained model is used to extract the word sense features, and the self-attention mechanism, refined highway, hybrid expert and capsule network are used to strengthen the word sense features respectively. The English MyPersonality dataset has an average accuracy of 63.01%, the Chinese MyPersonality dataset has an average accuracy of 62.64%, the English Essays dataset has an average accuracy of 70.16%, and the Chinese Essays dataset has an average accuracy of 70.08%, 65.46% average accuracy in English FriendsPersona, 62.57% average accuracy in Chinese FriendsPersona, 64.79% average accuracy in English Fusion dataset, and 65.46% average accuracy in Chinese Fusion dataset.
致謝詞 i
中文摘要 iii
Abstract iv
目錄 vi
圖目錄 ix
表目錄 x
第一章 緒論 1
1.1 前言 1
1.2 研究背景與動機 1
1.3 研究目的 2
1.4 論文架構 2
2 第二章 文獻探討 3
2.1 聊天機器人技術(Chatbot Technology) 4
2.1.1 檢索式聊天機器人技術(Retrieval Chatbot Technology) 4
2.1.2 聊天機器人導入個性(Chatbot with Personality Traits) 4
2.1.3 聊天機器人的評估(Evaluation of Chatbot) 5
2.1.4 聊天機器人的評估資料集(Evaluation Dataset of Chatbot) 5
2.2 注意力機制(Attention Mechanism) 6
2.2.1 自注意力(Self-Attention) 6
2.2.2 超長變形金剛(Transformer-XL) 6
2.2.3 開關變形金剛(Switch Transformer) 7
2.2.4 精緻高速網路(Refined Highway) 8
2.3 XLNet 8
2.4 膠囊網路(Capsule Network) 9
2.5 自動人格特質辨識 10
2.5.1 人格特質指標 10
2.5.2 自動人格特質辨識資料集Dataset 10
2.5.3 自動人格特質辨識 10
3 第三章 研究方法 12
3.1 探討中文與英文的表現差異 12
3.2 添加停用詞、標點符號、語助詞特徵進行辨識 13
3.3 架構1 - XLNet Self-Attention Caps 14
3.4 架構2 - XLNet Refined Highway Caps 15
3.5 架構3 - XLNet Switch Refined Highway Caps 16
3.6 有個性之聊天機器人 17
4 第四章 實驗結果 20
4.1 實驗環境 20
4.2 詞相似度、詞類比、文件分類資料配置 21
4.3 自動人格特質辨識訓練資料配置與驗證標準 21
4.4 聊天機器人主觀評估 21
4.5 自動人格特質辨識模型訓練超參數設定 21
4.6 提出方法之實驗結果驗證與比較 22
4.6.1 探討中文與英文的表現 22
4.6.2 實驗加入停用詞、標點符號、語助詞特徵之效果 23
4.6.3 自動人格特質辨識與相關文獻比較 25
4.6.4 聊天機器人檢索系統與相關文獻比較 28
4.6.5 聊天機器人個性匹配比較 29
4.7 模型改進分析與實驗 29
4.7.1 精緻高速公路(Refined Highway)層數實驗 29
4.7.2 Refined Highway與Switching layer連接實驗 31
4.7.3 去除膠囊網路(capsule network) 34
5 第五章 結論與未來展望 37
6 參考資料 38

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