博碩士論文 110423068 詳細資訊




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姓名 林佑錡(Yu-Chi Lin)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱
(History Aware Multi-Stage Prompting for Neural Chat 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 (2025-8-1以後開放)
摘要(中) 神經網路聊天翻譯 (Neural Chat Translation, NCT) 是近年於機器翻譯領域中興起的任務,與神經機器翻譯 (Neural Machine Translation, NMT) 不同的是,神經網路聊天翻譯還涉及了多輪對話,因此是一項相當具挑戰性的二合一任務。雖然先前已經有研究使用上下文感知模型,並加入不同的輔助任務來來解決此任務,但往往需要很高的訓練成本。
在微調預訓練語言的成本逐漸提升下,提示 (Prompt) 調整的開始興起,該方法展現了具備參數效率以及在表現上可與微調預訓練語言比較的特性。而最近此方法有被應用至機器翻譯領域中,但是仍只考慮句子級別的翻譯,沒辦法有效將神經網路聊天翻譯任務重視的聊天內容納入考量。因此在本研究中,我們為這項任務提出一個新的提示調整方法稱為 History Aware Multi-stage Prompting (HAMSP),透過將聊天歷史內容資訊納入到提示,以引導預訓練語言模型生成與對話情境一致的翻譯結果。
在實驗結果中,我們展示了我們提出的 HAMSP 與基準方法相較之下達到更好的表現性能,並且能夠與微調方法相互抗衡。而透過進一步的內在評估,我們說明了我們的方法更加的穩健,並且能夠有效提升翻譯結果的對話連貫性,以及可以提升訓練效率與降低硬體成本,具備廣泛應用至真實世界中不同的聊天系統之潛力。
摘要(英) Neural Chat Translation (NCT) is an emerging task in the field of machine translation. Unlike Neural Machine Translation (NMT), NCT involves multi-turn conversations, making it a challenging dual-task. Previous research has explored the use of context-aware models and auxiliary tasks to address this task, but often at a high training cost.
As the cost of fine-tuning pre-trained language models continues to rise, prompt tuning has emerged as a promising alternative. This method demonstrates the characteristics of parameter efficiency and comparable performance to fine-tuning pre-trained language models. Recently, this method has been applied to the field of machine translation, but it only considers sentence-level translations and does not incorporate the conversational content that is crucial in neural chat translation tasks. Therefore, in this study, we present a new prompt tuning method called History Aware Multi-Stage Prompting (HAMSP). By incorporating the information from the chat history into the prompts, we guide the pre-trained language model to generate translations that are consistent with the conversational context.
In the experimental results, we demonstrate that our proposed HAMSP outperforms the baseline methods and can compete with fine-tuning methods. Through further intrinsic evaluation, we illustrate the robustness of our method and its ability to enhance the dialogue coherence of translations. Additionally, our method shows potential for improving training efficiency and reducing hardware costs, making it suitable for various chat systems in real-world applications.
關鍵字(中) ★ 神經網路聊天翻譯
★ 機器翻譯
★ 提示調整
★ 深度學習
關鍵字(英) ★ neural chat translation
★ machine translation
★ prompt tuning
★ deep learning
論文目次 摘要 I
Abstract II
Acknowledgements III
Table of Contents IV
List of Figures VI
List of Tables VII
1. Introduction 1
1.1. Overview 1
1.2. Motivation 2
1.3. Objectives 4
1.4. Thesis Organization 5
2. Related Works 6
2.1. Neural Machine Translation 6
2.1.1. Sentence-level NMT 6
2.1.2. Document-level NMT 8
2.1.3. Neural Chat Translation 10
2.2. Prompt Tuning 12
2.2.1. Manual Prompt 13
2.2.2. Discrete Prompt 14
2.2.3. Continuous Prompt 20
2.3. Multilingual Pre-trained Language Models 28
2.3.1. mBART 28
2.3.2. mT5 29
2.3.3. mGPT 30
2.4. Discussion 31
3. Methodology 34
3.1. Model Overview 34
3.2. Model Architecture 35
3.2.1. Prompt Generator 36
3.2.2. Multi-Stage 38
3.3. Training Phase 39
3.4. Datasets 39
3.5. Experiment setting 41
3.5.1. Data preprocessing and postprocessing 41
3.5.2. Model Setting 41
3.6. Flow Chart 43
3.7. Experiment Design 43
3.7.1. Experiment - The effectiveness of our proposed prompting method applied to NCT tasks. 43
3.7.2. Evaluation Metrics 44
4. Experiment Results 48
4.1. Experiment - The effectiveness of our proposed prompting method applied to NCT tasks. 48
4.1.1. Experiment Results 48
4.1.2. Intrinsic evaluation 56
5. Conclusion 72
5.1. Overall summary 72
5.2. Contributions 72
5.3. Study limitation 73
5.4. Future work 74
Reference 75
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指導教授 柯士文(Shin-Wen Ke) 審核日期 2023-7-20
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