請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71401完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李琳山 | |
| dc.contributor.author | Ching-Ting Chang | en |
| dc.contributor.author | 張瀞婷 | zh_TW |
| dc.date.accessioned | 2021-06-17T06:00:06Z | - |
| dc.date.available | 2021-02-19 | |
| dc.date.copyright | 2019-02-19 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-02-12 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71401 | - |
| dc.description.abstract | 語碼轉換是指在一段話或是一段文字中交替使用兩種以上語言。基本上不同語者、不同對話內容、不同語言組合都可能有不同的語碼轉換風格與特性。雖然語碼轉換在自然語言中出現頻繁，但相較於單一語言，語碼轉換的語料相當缺乏。
本論文目標是發展一種非監督式的技術來自動產生語碼轉換的語料，並在兩套中文為主位語言、英文為客位語言的語碼轉換資料集上實驗驗證。本論文的方法是藉由生成對抗網路以及梯度策略演算法，從單一語言的文句 (主位語言) 預測適合的語碼轉換位置，將這些位置以詞翻譯為客位語言後產生句內語碼轉換的文句，並用以作為語言模型的增強訓練語料。結果顯示本論文所提出的方式能夠小幅度改善語言模型，並小幅降低語音辨識系統的客位語言的錯誤率。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-17T06:00:06Z (GMT). No. of bitstreams: 1 ntu-108-R05942066-1.pdf: 1707452 bytes, checksum: bdfaef0804f4ee25c4acd71aec5571d3 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 一、導論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 研究動機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 相關研究 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 研究方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 章節安排 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 二、背景知識 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1 語碼轉換 (Code-switching, CS) . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 起源及定義 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.2 資料特性 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 機器學習 (Machine Learning, ML) . . . . . . . . . . . . . . . . . . . . 9 2.2.1 類神經網路 (Neural Networks, NN) . . . . . . . . . . . . . . . . 9 2.2.2 遞迴式類神經網路 (Recurrent Neural Networks, RNN) . . . . . 13 2.2.3 生成對抗網路 (Generative Adversarial Networks, GAN) . . . . 16 2.2.4 策略梯度 (Policy Gradient) 演算法 . . . . . . . . . . . . . . . . 17 2.3 語言模型及語音辨識系統 . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.1 語音辨識系統 (Automatic Speech Recognition, ASR) . . . . . . 19 2.3.2 語言模型 (Language Model, LM) . . . . . . . . . . . . . . . . . 20 2.3.3 聲學模型 (Acoustic Model, AM) . . . . . . . . . . . . . . . . . 23 2.3.4 音素集 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.5 辭典 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.6 辨識解碼 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4 本章總結 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 三、中英文語碼轉換位置預測 . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.1 簡介 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2 實驗語料 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3 實驗模型 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4 評量方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.4.1 資訊檢索評估機制 . . . . . . . . . . . . . . . . . . . . . . . . 36 3.4.2 BLEU 値 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.4.3 詞錯誤率 (Word error rate, WER) . . . . . . . . . . . . . . . . . 38 3.5 實驗結果及分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.5.1 實驗基準 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.5.2 實驗數據 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.5.3 結果範例 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.5.4 結果分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.6 本章總結 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 四、中英文語碼轉換文本生成之應用 . . . . . . . . . . . . . . . . . . . . . . . 46 4.1 簡介 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.2 實驗語料 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.3 生成文本之品質‒‒以混淆度評量 . . . . . . . . . . . . . . . . . . . . . 46 4.3.1 實驗模型 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.3.2 評量方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.3.3 實驗結果及分析 . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4 以生成文本訓練語言模型所獲得之混淆度 . . . . . . . . . . . . . . . 50 4.4.1 實驗模型 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4.2 評量方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4.3 實驗結果及分析 . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.5 以生成文本訓練語言模型用至語音辨識系統 . . . . . . . . . . . . . . 53 4.5.1 實驗模型 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.5.2 評量方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.5.3 實驗結果及分析 . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.6 生成文句之實例 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.7 本章總結 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 五、結論與展望 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.1 總結討論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.2 未來展望 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 參考文獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 | |
| dc.language.iso | zh-TW | |
| dc.subject | 語碼轉換 | zh_TW |
| dc.subject | 文本生成 | zh_TW |
| dc.subject | 資料增強 | zh_TW |
| dc.subject | 語言模型 | zh_TW |
| dc.subject | 生成對抗網路 | zh_TW |
| dc.subject | data augmentation | en |
| dc.subject | language modeling | en |
| dc.subject | code-switching | en |
| dc.subject | text generation | en |
| dc.subject | generative adversarial networks | en |
| dc.title | 以生成對抗網路自動產生中英文語碼轉換文句 | zh_TW |
| dc.title | Chinese-English Code-switching Sentence Generation by Generative Adversarial Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李宏毅,蘇炫榮 | |
| dc.subject.keyword | 語碼轉換,文本生成,資料增強,語言模型,生成對抗網路, | zh_TW |
| dc.subject.keyword | code-switching,text generation,data augmentation,language modeling,generative adversarial networks, | en |
| dc.relation.page | 74 | |
| dc.identifier.doi | 10.6342/NTU201900420 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-02-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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