博碩士論文 108552015 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:18 、訪客IP:18.208.187.128
姓名 呂昕恩(Sin-En Lu)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 基於台語與華語之語碼混合資料集與翻譯模型
(Hokkien-Mandarin Code-Mixing Dataset and Neural Machine Translation)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2023-12-31以後開放)
摘要(中) 台語與中文語碼混合在台灣是一個常見的口語現象,然而台灣遲至 21 世紀才開始建立官方書寫系統。缺少官方書寫系統,不僅代表著我們在 NLP 領域面臨資源不足的問題,導致我們在方言代碼混合任務上難以取得突破性研究,更意味著我們面臨著語言傳承的困難。基於上述問題,本研究將從簡要介紹台語的歷史以及台灣語碼混合現象著手,討論台灣語碼混合的語言比例組成與文法結構,建立基於台文字的台語語華語之語碼混合資料集,並介紹可應用於台文的現有斷詞工具。
同時我們將在本研究介紹台語語言模型的訓練方法,同時使用我們提出的資料集,利用 XLM 開發台語語碼混合翻譯模型。

為適用於語碼混合的情境,我們提出自動化語言標注(DLI)機制,並使用遷移學習提升翻譯模型表現。
最後我們根據交叉熵(Cross-entropy, CE)的問題,提出三種利用詞彙詞相似度來重構損失函數。我們提出 WBI 機制,解決詞彙資訊與字符集預訓練模型不相容的問題,並引入 WordNet 知識在模型中。與標準 CE 相比,在單語和語碼混資料集的實驗結果表明,我們的最佳損失函數在單語和 CM 在 BLEU 上,分別進步 2.42分(62.11 到 64.53)和 0.7(62.86 到 63.56)分。我們的實驗證明即使是使用基於字符訓練的語言模型,我們可以將辭彙的資訊攜帶到下游任務中。
摘要(英) Code-mixing is a complicated task in Natural Language Processing (NLP), especially for mixed languages are dialects. In Taiwan, code-mixing is a common phenomenon. The most popular code-mixed language pair is Hokkien and Mandarin. However, there is a lack of resources in Hokkien. Therefore, we proposed a Hokkien-Mandarin code-mixing dataset and offered the efficient Hokkien word segment method through an open-source toolkit. These could overcome the morphology issue under the Sino-Tibetan family. We modify an XLM model (cross-lingual language model) with the dynamic language identify (DLI) mechanism and use transfer learning to train our proposed dataset on translation tasks. We found that by applying language knowledge, rules and offering the language tags, the model achieves good performance on code-mixing data translation results and maintains the quality of monolingual translation.

Recently, most neural machine translation models (NMT) use cross-entropy as the loss function, including XLM model. However, standard cross-entropy penalizes the model when it fails to generate ground truth answers, eliminating the opportunity to consider other possibilities. It can cause problems with extit{overcorrection} or extit{over-confident}. Some solutions to reconstruct the loss function using word similarity have been proposed. But these solutions are not suitable for Chinese because most Chinese models are pre-trained at the character level. In this work, we propose a simple but effective method, Word Boundary Insertion (WBI), to address the inconsistency between word-level and character-level by reconstructing the loss function of Chinese NMT models. WBI considers word similarity without modifying or retraining a new language model. We propose three modified loss functions for use with XLM, and the calculation of these loss functions also refers to the WordNet. Compared with the standard cross-entropy, experimental results on both monolingual and code-mixing (code-mixing) Hokkien-Chinese datasets show that our best loss function achieves BLEU score improvements of 2.42 (62.11 to 64.53) and 0.7 (62.86 to 63.56) on monolingual and code-mixing data, respectively.
關鍵字(中) ★ 語碼混合
★ 機器翻譯
★ 損失函數重構
★ 低資源語言
關鍵字(英) ★ Code-Mixing
★ Neural Machine Translation
★ Loss Function Reconstruction
★ Low Resource
★ WordNet
論文目次 中文摘要 v
Abstract vi
致謝 viii
Contents x
List of Figures xiii
List of Tables xiv
1 Introduction 1
1.1 Motivate . 1
1.1.1 Code-Mixing 1
1.1.2 Neural Machine Translation 2
1.2 Goal . 3
2 Background of Taiwanese Hokkien 5
2.1 History of Taiwanese Hokkien . 5
2.2 Taiwanese Hokkien Writing System and Difficulties 6
2.3 Difficulties in Written Taiwanese Hokkien . 9
2.3.1 Ambiguous Word Boundary in Written Taiwanese Hokkien 9
2.3.2 Literary and Colloquial Readings . 10
2.3.3 Insufficient Resource 11
x
2.4 Recap 11
3 Related Work 12
3.1 Code-mixing Theory 12
3.2 Code-Mixing Research in Taiwanese Hokkien 14
3.3 Code-Mixed Corpus 16
3.4 Code-Mixed Translation 18
3.5 Pre-trained Language Models . 20
3.6 Transfer Learning . 22
3.7 Chinese Language Model and Machine Translation 23
3.8 Recap 24
4 Dataset and Evaluation 25
4.1 Data source . 25
4.2 Synthetic Hokkien-Madarin Code-mixed Data . 26
4.2.1 Problems in using Chinese Toolkit . 27
4.2.2 Articut : Solution of Hokkien Word Segment . 29
4.2.3 ( CHECK DONE ) Synthetic Approach 31
4.3 Data Analysis 32
4.3.1 Human Scoring . 33
4.3.2 Code-Mixied Complexity . 35
4.3.3 Inter-rater score . 35
4.4 Recap 36
5 Hokkien Language Model and Translation System 37
5.1 Assumption . 37
5.2 Hokkien Language Model . 37
5.3 XLM Model . 39
5.3.1 Dynamic Language Identify and AutoEncoder 40
5.3.2 Transfer Learning 41
5.4 Loss Function 42
xi
5.4.1 Word-Boundary Insertion (WBI) . 43
5.4.2 Word Similarity . 44
5.4.3 Loss Function Modification 45
5.5 Recap 48
6 Experiment and Result 49
6.1 Dataset, Baseline, and Evaluation Metrics . 49
6.2 Experiment Settings . 50
6.3 Metrics . 50
6.4 Hokkien Language Model and XLM 51
6.5 Dynamic Language Identity and AutoEncoder . 52
6.6 Transfer Learning 53
6.7 Loss function 53
6.8 Recap 55
7 Loss Function Analysis 56
7.1 Similar-word Configuration . 56
7.2 Proposed Loss Function . 56
7.3 Case Study . 57
7.3.1 Similar-word Configuration 57
7.3.2 Proposed Loss Function 59
7.4 Recap 60
8 Conclusion and Future Work 61
8.1 Conclusion . 61
8.2 Future Work . 62
Bibliography 64
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指導教授 蔡宗翰(Richard Tzong-Han Tsai) 審核日期 2022-1-21
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